Learning_pandas.ipynb

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      "source": [
        "## <font size=\"6\">↦</font> **Import** the pandas library as pd (standard convention)."
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      "source": [
        "import pandas as pd"
      ]
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      "source": [
        "# DataFrame\n",
        "\n",
        "\n",
        "---\n",
        "---\n",
        "\n",
        "A DataFrame is a table. It contains an array of individual entries, each of which has a certain value. Each entry corresponds to a row (or record) and a column."
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        "pd.DataFrame({\"Yes\" : [50, 21], \"No\" : [131, 2]})"
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              "   Yes   No\n",
              "0   50  131\n",
              "1   21    2"
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        "In this example, the \"0, No\" entry has the value of 131. The \"0, Yes\" entry has a value of 50, and so on.\n",
        "<br><br>\n",
        "DataFrame entries are not limited to integers. For instance, here's a DataFrame whose values are strings. \n",
        "\n",
        "The dictionary-list constructor assigns values to the column labels, but just uses an ascending count from 0 (0, 1, 2, 3, ...) for the row labels. Sometimes this is OK, but oftentimes we will want to assign these labels ourselves.\n",
        "\n",
        "The list of row labels used in a DataFrame is known as an *index*. We can assign values to it by using an index parameter in our constructor:"
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        "pd.DataFrame({'Bob': ['I liked it.', 'It was awful.'], \n",
        "              'Sue': ['Pretty good.', 'Bland.']},\n",
        "             index=['Product A', 'Product B'])"
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              "                     Bob           Sue\n",
              "Product A    I liked it.  Pretty good.\n",
              "Product B  It was awful.        Bland."
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        "#Series\n",
        "\n",
        "---\n",
        "---\n",
        "\n",
        "A Series, by contrast, is a sequence of data values. If a DataFrame is a table, a Series is a list. And in fact you can create one with nothing more than a list:"
      ],
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        "id": "KguuJB4RQgSu"
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      "cell_type": "code",
      "source": [
        "pd.Series([1,1,2,3,5,8])"
      ],
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        "id": "aE_UchT3Qd-A",
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            "text/plain": [
              "0    1\n",
              "1    1\n",
              "2    2\n",
              "3    3\n",
              "4    5\n",
              "5    8\n",
              "dtype: int64"
            ]
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          "metadata": {},
          "execution_count": 4
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    {
      "cell_type": "markdown",
      "source": [
        "A Series is, in essence, a single column of a DataFrame. So you can assign row labels to the Series the same way as before, using an index parameter. However, a Series does not have a column name, it only has one overall name:"
      ],
      "metadata": {
        "id": "Nvogv6JqRBLB"
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      "cell_type": "code",
      "source": [
        "pd.Series([30, 35, 40], \n",
        "          index=['2015 Sales', '2016 Sales', '2017 Sales'], \n",
        "          name='Product A')     # Single column of DataFrame"
      ],
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      "execution_count": null,
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            "text/plain": [
              "2015 Sales    30\n",
              "2016 Sales    35\n",
              "2017 Sales    40\n",
              "Name: Product A, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
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    },
    {
      "cell_type": "markdown",
      "source": [
        "#Reading data files\n",
        "\n",
        "---\n",
        "---\n",
        "\n",
        "Being able to create a DataFrame or Series by hand is handy. But, most of the time, we won't actually be creating our own data by hand. Instead, we'll be working with data that already exists.\n",
        "<br><br>\n"
      ],
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      "source": [
        "\n",
        "---\n",
        "##CSV\n",
        "\n",
        "Data can be stored in any of a number of different forms and formats. By far the most basic of these is the humble CSV file. When you open a CSV file you get something that looks like this:\n",
        "\n",
        "`Product A,Product B,Product C,30,21,9,35,34,1,41,11,11`\n",
        "\n",
        "So a CSV file is a table of values separated by commas. Hence the name: \"Comma-Separated Values\", or CSV.\n",
        "\n",
        "Let's now set aside our toy datasets and see what a real dataset looks like when we read it into a DataFrame. We'll use the <tt><strong>pd.read_csv()</strong></tt> function to read the data into a DataFrame. This goes thusly:"
      ],
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    {
      "cell_type": "code",
      "source": [
        "housing_data = pd.read_csv(\"sample_data/california_housing_train.csv\")"
      ],
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        "id": "X4_IwygNS7H_"
      },
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      "source": [
        "housing_data.shape          # Structure of DataFrame"
      ],
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          "data": {
            "text/plain": [
              "(17000, 9)"
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          "execution_count": 7
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      "cell_type": "code",
      "source": [
        "housing_data.head()         # Return the first n rows. Default: n = 5."
      ],
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            "text/plain": [
              "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0    -114.31     34.19                15.0       5612.0          1283.0   \n",
              "1    -114.47     34.40                19.0       7650.0          1901.0   \n",
              "2    -114.56     33.69                17.0        720.0           174.0   \n",
              "3    -114.57     33.64                14.0       1501.0           337.0   \n",
              "4    -114.57     33.57                20.0       1454.0           326.0   \n",
              "\n",
              "   population  households  median_income  median_house_value  \n",
              "0      1015.0       472.0         1.4936             66900.0  \n",
              "1      1129.0       463.0         1.8200             80100.0  \n",
              "2       333.0       117.0         1.6509             85700.0  \n",
              "3       515.0       226.0         3.1917             73400.0  \n",
              "4       624.0       262.0         1.9250             65500.0  "
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              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-114.31</td>\n",
              "      <td>34.19</td>\n",
              "      <td>15.0</td>\n",
              "      <td>5612.0</td>\n",
              "      <td>1283.0</td>\n",
              "      <td>1015.0</td>\n",
              "      <td>472.0</td>\n",
              "      <td>1.4936</td>\n",
              "      <td>66900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-114.47</td>\n",
              "      <td>34.40</td>\n",
              "      <td>19.0</td>\n",
              "      <td>7650.0</td>\n",
              "      <td>1901.0</td>\n",
              "      <td>1129.0</td>\n",
              "      <td>463.0</td>\n",
              "      <td>1.8200</td>\n",
              "      <td>80100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-114.56</td>\n",
              "      <td>33.69</td>\n",
              "      <td>17.0</td>\n",
              "      <td>720.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>333.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>1.6509</td>\n",
              "      <td>85700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.64</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1501.0</td>\n",
              "      <td>337.0</td>\n",
              "      <td>515.0</td>\n",
              "      <td>226.0</td>\n",
              "      <td>3.1917</td>\n",
              "      <td>73400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.57</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1454.0</td>\n",
              "      <td>326.0</td>\n",
              "      <td>624.0</td>\n",
              "      <td>262.0</td>\n",
              "      <td>1.9250</td>\n",
              "      <td>65500.0</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Custom or Predefined Index"
      ],
      "metadata": {
        "id": "KS-atmVdXHIH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data = pd.read_csv(\"sample_data/california_housing_train.csv\", index_col=0)\n",
        "housing_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "pbs-pqujXTFR",
        "outputId": "4866f75c-cb1a-499f-a892-0a929bc91cb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "longitude                                                              \n",
              "-114.31       34.19                15.0       5612.0          1283.0   \n",
              "-114.47       34.40                19.0       7650.0          1901.0   \n",
              "-114.56       33.69                17.0        720.0           174.0   \n",
              "-114.57       33.64                14.0       1501.0           337.0   \n",
              "-114.57       33.57                20.0       1454.0           326.0   \n",
              "\n",
              "           population  households  median_income  median_house_value  \n",
              "longitude                                                             \n",
              "-114.31        1015.0       472.0         1.4936             66900.0  \n",
              "-114.47        1129.0       463.0         1.8200             80100.0  \n",
              "-114.56         333.0       117.0         1.6509             85700.0  \n",
              "-114.57         515.0       226.0         3.1917             73400.0  \n",
              "-114.57         624.0       262.0         1.9250             65500.0  "
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame({\"Apples\" : [30], \"Bananas\" : [21]})\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "KD8K-cFYYwEx",
        "outputId": "1d253ab7-f55f-4484-dccd-5c66a91b1a07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Apples  Bananas\n",
              "0      30       21"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>Apples</th>\n",
              "      <th>Bananas</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>30</td>\n",
              "      <td>21</td>\n",
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              "</table>\n",
              "</div>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4fb2e87b-7ab3-45cc-b076-c15f219a393f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Saving Files"
      ],
      "metadata": {
        "id": "07z8V9NAbkcG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fruits = pd.DataFrame({\"Apples\":[10,23,56], \"Bananas\":[46,89,145]},index=[\"Farm1\",\"Farm2\",\"Farm3\"])\n",
        "fruits.to_csv(\"fruits.csv\")"
      ],
      "metadata": {
        "id": "MrqOKg6wbjn6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Accessing\n",
        "\n",
        "---\n",
        "---\n",
        "Selecting specific values of a pandas DataFrame or Series to work on is an implicit step in almost any data operation. You need to learn in working with data in Python is how to go about selecting the data points relevant to you quickly and effectively.\n",
        "\n",
        "<br>"
      ],
      "metadata": {
        "id": "HcRny7y3c3mj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "##Naive Objects\n",
        "\n",
        "In Python, we can access the property of an object by accessing it as an attribute. A book object, for example, might have a title property, which we can access by calling book.title. Columns in a pandas DataFrame work in much the same way."
      ],
      "metadata": {
        "id": "BIYVXDssdJPr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data = pd.read_csv(\"sample_data/california_housing_train.csv\")\n",
        "housing_data.total_rooms"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pkZEcHjMdIAT",
        "outputId": "a4299a50-7265-4f3a-a736-fe039a79d952"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        5612.0\n",
              "1        7650.0\n",
              "2         720.0\n",
              "3        1501.0\n",
              "4        1454.0\n",
              "          ...  \n",
              "16995    2217.0\n",
              "16996    2349.0\n",
              "16997    2677.0\n",
              "16998    2672.0\n",
              "16999    1820.0\n",
              "Name: total_rooms, Length: 17000, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data[\"total_rooms\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v8t49IMceyD1",
        "outputId": "b499b62b-da3b-4e9c-b78c-2ba7204476e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5612.0"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "##Indexing\n",
        "\n",
        "The indexing operator and attribute selection are nice because they work just like they do in the rest of the Python ecosystem. As a novice, this makes them easy to pick up and use. However, pandas has its own accessor operators, loc and iloc. "
      ],
      "metadata": {
        "id": "mjZHTrt7fgK1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Index-based selection\n",
        "Pandas indexing works in one of two paradigms. The first is index-based selection: **selecting data based on its numerical position in the data**. iloc follows this paradigm.\n",
        "\n",
        "To select the first row of data in a DataFrame, we may use the following:"
      ],
      "metadata": {
        "id": "qZC_IVTMf23n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.iloc[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z4FMQxBWfxIH",
        "outputId": "d107af7f-413d-4255-c15a-ec27a728c642"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "longitude              -114.3100\n",
              "latitude                 34.1900\n",
              "housing_median_age       15.0000\n",
              "total_rooms            5612.0000\n",
              "total_bedrooms         1283.0000\n",
              "population             1015.0000\n",
              "households              472.0000\n",
              "median_income             1.4936\n",
              "median_house_value    66900.0000\n",
              "Name: 0, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Both loc and iloc are row-first, column-second. This is the **opposite** of what we do in native Python, which is column-first, row-second.\n",
        "\n",
        "This means that it's marginally easier to retrieve rows, and marginally harder to get retrieve columns. To get a column with iloc, we can do the following:"
      ],
      "metadata": {
        "id": "i_bDvbbXge8G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.iloc[:, 0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OY0ewikfgjPO",
        "outputId": "acb8a75f-fd95-4df6-8f48-c4de3c241ed2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       -114.31\n",
              "1       -114.47\n",
              "2       -114.56\n",
              "3       -114.57\n",
              "4       -114.57\n",
              "          ...  \n",
              "16995   -124.26\n",
              "16996   -124.27\n",
              "16997   -124.30\n",
              "16998   -124.30\n",
              "16999   -124.35\n",
              "Name: longitude, Length: 17000, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "On its own, the : operator, which also comes from native Python, means \"everything\". When combined with other selectors, however, it can be used to indicate a range of values. For example, to select the households column from just the first, second, and third row, we would do:"
      ],
      "metadata": {
        "id": "ZRTKNv6Eg6Y1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.iloc[:3, 6]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U2JbToVUhQFA",
        "outputId": "12f4651b-a4fb-41ec-b4d4-5bd05c31724c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    472.0\n",
              "1    463.0\n",
              "2    117.0\n",
              "Name: households, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.iloc[-2:]          #last 2 rows"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "nmGsmHdOhh_U",
        "outputId": "10bebada-572f-4b80-d5ac-26ceaf442e5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "16998    -124.30     41.80                19.0       2672.0           552.0   \n",
              "16999    -124.35     40.54                52.0       1820.0           300.0   \n",
              "\n",
              "       population  households  median_income  median_house_value  \n",
              "16998      1298.0       478.0         1.9797             85800.0  \n",
              "16999       806.0       270.0         3.0147             94600.0  "
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              "                                                     [key], {});\n",
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              "\n",
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            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Label-based selection\n",
        "The second paradigm for attribute selection is the one followed by the loc operator: label-based selection. In this paradigm, it's the data index value, not its position, which matters.\n",
        "\n",
        "For example, to get the first entry in DataFrame, we would now do the following:"
      ],
      "metadata": {
        "id": "rksE8Pxph05d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.loc[0, \"households\"]"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "KPPTtkpwiQx_",
        "outputId": "37dae8f2-4251-47f0-cfa9-b306f90a3ea5"
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "472.0"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
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        "housing_data.loc[:, [\"longitude\",\"latitude\",\"households\"]]"
      ],
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      "execution_count": null,
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            "text/plain": [
              "       longitude  latitude  households\n",
              "0        -114.31     34.19       472.0\n",
              "1        -114.47     34.40       463.0\n",
              "2        -114.56     33.69       117.0\n",
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              "...          ...       ...         ...\n",
              "16995    -124.26     40.58       369.0\n",
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              "16997    -124.30     41.84       456.0\n",
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              "16999    -124.35     40.54       270.0\n",
              "\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "iloc uses the Python stdlib indexing scheme, where the first element of the range is included and the last one excluded. So 0:10 will select entries 0,...,9. loc, meanwhile, indexes inclusively. So 0:10 will select entries 0,...,10."
      ],
      "metadata": {
        "id": "2sue9bzWiyyw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.loc[0:2, \"households\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qK0h6ipMi0cS",
        "outputId": "2b406b33-2140-4421-85f9-207ca7bb323f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    472.0\n",
              "1    463.0\n",
              "2    117.0\n",
              "Name: households, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.iloc[0:2, 6]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QsPWvRlPjdvM",
        "outputId": "592d6356-8a85-412d-a6c9-5b934e0ac4c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    472.0\n",
              "1    463.0\n",
              "Name: households, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Manipulating the index\n",
        "\n",
        "Label-based selection derives its power from the labels in the index. Critically, the index we use is not immutable. We can manipulate the index in any way we see fit.\n",
        "\n",
        "The **set_index()** method can be used to do the job. Here is what happens when we set_index to the households field:"
      ],
      "metadata": {
        "id": "sNTpTUtqj27W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.set_index(\"households\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "FEqQ9punkIW_",
        "outputId": "cbb7e11a-357c-4246-de69-2b10fe65f675"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            longitude  latitude  housing_median_age  total_rooms  \\\n",
              "households                                                         \n",
              "472.0         -114.31     34.19                15.0       5612.0   \n",
              "463.0         -114.47     34.40                19.0       7650.0   \n",
              "117.0         -114.56     33.69                17.0        720.0   \n",
              "226.0         -114.57     33.64                14.0       1501.0   \n",
              "262.0         -114.57     33.57                20.0       1454.0   \n",
              "...               ...       ...                 ...          ...   \n",
              "369.0         -124.26     40.58                52.0       2217.0   \n",
              "465.0         -124.27     40.69                36.0       2349.0   \n",
              "456.0         -124.30     41.84                17.0       2677.0   \n",
              "478.0         -124.30     41.80                19.0       2672.0   \n",
              "270.0         -124.35     40.54                52.0       1820.0   \n",
              "\n",
              "            total_bedrooms  population  median_income  median_house_value  \n",
              "households                                                                 \n",
              "472.0               1283.0      1015.0         1.4936             66900.0  \n",
              "463.0               1901.0      1129.0         1.8200             80100.0  \n",
              "117.0                174.0       333.0         1.6509             85700.0  \n",
              "226.0                337.0       515.0         3.1917             73400.0  \n",
              "262.0                326.0       624.0         1.9250             65500.0  \n",
              "...                    ...         ...            ...                 ...  \n",
              "369.0                394.0       907.0         2.3571            111400.0  \n",
              "465.0                528.0      1194.0         2.5179             79000.0  \n",
              "456.0                531.0      1244.0         3.0313            103600.0  \n",
              "478.0                552.0      1298.0         1.9797             85800.0  \n",
              "270.0                300.0       806.0         3.0147             94600.0  \n",
              "\n",
              "[17000 rows x 8 columns]"
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              "\n",
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              "      <th>472.0</th>\n",
              "      <td>-114.31</td>\n",
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              "      <th>226.0</th>\n",
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              "      <td>394.0</td>\n",
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              "      <th>478.0</th>\n",
              "      <td>-124.30</td>\n",
              "      <td>41.80</td>\n",
              "      <td>19.0</td>\n",
              "      <td>2672.0</td>\n",
              "      <td>552.0</td>\n",
              "      <td>1298.0</td>\n",
              "      <td>1.9797</td>\n",
              "      <td>85800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>270.0</th>\n",
              "      <td>-124.35</td>\n",
              "      <td>40.54</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1820.0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>806.0</td>\n",
              "      <td>3.0147</td>\n",
              "      <td>94600.0</td>\n",
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              "<p>17000 rows × 8 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9830696e-8c32-4d3c-9130-a5d8defa8f23')\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          document.querySelector('#df-9830696e-8c32-4d3c-9130-a5d8defa8f23 button.colab-df-convert');\n",
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              "          const element = document.querySelector('#df-9830696e-8c32-4d3c-9130-a5d8defa8f23');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "##Conditional selection\n",
        "So far we've been indexing various strides of data, using structural properties of the DataFrame itself. To do interesting things with the data, however, we often need to ask questions based on conditions."
      ],
      "metadata": {
        "id": "kXISHcoEkZQD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data = pd.read_csv(\"sample_data/california_housing_train.csv\")\n",
        "housing_data.total_rooms >= 7000"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2jZBCIJ6keOz",
        "outputId": "8587914e-ae77-4ddc-d64f-5272b7e2822e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        False\n",
              "1         True\n",
              "2        False\n",
              "3        False\n",
              "4        False\n",
              "         ...  \n",
              "16995    False\n",
              "16996    False\n",
              "16997    False\n",
              "16998    False\n",
              "16999    False\n",
              "Name: total_rooms, Length: 17000, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This operation produced a Series of True/False booleans based on the total rooms of each record. This result can then be used inside of loc to select the relevant data:"
      ],
      "metadata": {
        "id": "ndDZSuYJlGsO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.loc[housing_data.total_rooms >= 7000]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "kmjbSkFPlK_T",
        "outputId": "8da45240-d00f-45b8-9356-c5ae4aac4954"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "1        -114.47     34.40                19.0       7650.0          1901.0   \n",
              "133      -116.06     34.15                15.0      10377.0          2331.0   \n",
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              "...          ...       ...                 ...          ...             ...   \n",
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              "16619    -122.72     38.58                 4.0       7042.0          1100.0   \n",
              "\n",
              "       population  households  median_income  median_house_value  \n",
              "1          1129.0       463.0         1.8200             80100.0  \n",
              "133        4507.0      1807.0         2.2466             66800.0  \n",
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              "168        5525.0      1845.0         2.7598             95000.0  \n",
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              "...           ...         ...            ...                 ...  \n",
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              "16490      3551.0      1392.0         4.6069            202300.0  \n",
              "16546      4323.0      1475.0         3.9518            220300.0  \n",
              "16619      2936.0      1043.0         5.0555            240800.0  \n",
              "\n",
              "[628 rows x 9 columns]"
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          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can use the \n",
        "\n",
        "{ & = AND } \n",
        "\n",
        "{ | = OR } \n",
        "\n",
        "to bring the two questions together:"
      ],
      "metadata": {
        "id": "RMOsjORaljJZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.loc[(housing_data.total_rooms >= 7000) & (housing_data.median_house_value >= 300000)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "RbCbIBmNljlq",
        "outputId": "53240f06-8e66-4284-8352-cac5947e35db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "172      -116.26     33.65                 3.0       7437.0          1222.0   \n",
              "187      -116.33     33.72                11.0      12327.0          2000.0   \n",
              "193      -116.37     33.69                 7.0       8806.0          1542.0   \n",
              "210      -116.42     33.76                14.0      16921.0          2837.0   \n",
              "647      -117.04     32.99                 6.0       9518.0          1418.0   \n",
              "...          ...       ...                 ...          ...             ...   \n",
              "16153    -122.47     37.89                23.0      10774.0          1736.0   \n",
              "16306    -122.50     37.91                31.0       7001.0          1282.0   \n",
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              "16474    -122.62     38.40                10.0       9772.0          1308.0   \n",
              "\n",
              "       population  households  median_income  median_house_value  \n",
              "172         574.0       302.0        10.2948            382400.0  \n",
              "187        2450.0      1139.0         7.4382            353100.0  \n",
              "193         858.0       448.0         7.8005            318100.0  \n",
              "210        2524.0      1262.0         7.6281            341700.0  \n",
              "647        4413.0      1275.0         6.6012            314900.0  \n",
              "...           ...         ...            ...                 ...  \n",
              "16153      3895.0      1683.0         7.2905            500001.0  \n",
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              "16474      3741.0      1242.0         6.5261            324700.0  \n",
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              "[157 rows x 9 columns]"
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              "    <tr>\n",
              "      <th>16474</th>\n",
              "      <td>-122.62</td>\n",
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              "      <td>3741.0</td>\n",
              "      <td>1242.0</td>\n",
              "      <td>6.5261</td>\n",
              "      <td>324700.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>157 rows × 9 columns</p>\n",
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    {
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      "source": [
        "housing_data.loc[(housing_data.total_rooms >= 7000) | (housing_data.median_house_value <= 100000)]"
      ],
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        "id": "fgbdX9ufmk1b",
        "outputId": "d880c0c6-c337-4949-98e5-87ca59313e78"
      },
      "execution_count": null,
      "outputs": [
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              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0        -114.31     34.19                15.0       5612.0          1283.0   \n",
              "1        -114.47     34.40                19.0       7650.0          1901.0   \n",
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              "...          ...       ...                 ...          ...             ...   \n",
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              "16999    -124.35     40.54                52.0       1820.0           300.0   \n",
              "\n",
              "       population  households  median_income  median_house_value  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pandas comes with a few built-in conditional selectors.\n",
        "<br><br>\n",
        "The first is **isin**. isin is lets you select data whose value \"is in\" a list of values. \n",
        "<br><br>\n",
        "<tt>== ∀𝑥</tt>"
      ],
      "metadata": {
        "id": "YOoozm7BmxBQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.loc[housing_data.housing_median_age.isin([15.0,16.0])]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "dd2sX4wxm4dQ",
        "outputId": "5cf97429-f01e-4a4a-de56-85da48252f9a"
      },
      "execution_count": null,
      "outputs": [
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              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0        -114.31     34.19                15.0       5612.0          1283.0   \n",
              "10       -114.60     33.62                16.0       3741.0           801.0   \n",
              "14       -114.63     32.76                15.0       1448.0           378.0   \n",
              "23       -114.98     33.82                15.0        644.0           129.0   \n",
              "32       -115.39     32.76                16.0       1136.0           196.0   \n",
              "...          ...       ...                 ...          ...             ...   \n",
              "16871    -123.75     39.37                16.0       1377.0           296.0   \n",
              "16876    -123.79     39.44                16.0       2017.0           423.0   \n",
              "16894    -124.00     40.22                16.0       2088.0           535.0   \n",
              "16957    -124.16     41.74                15.0       2715.0           569.0   \n",
              "16983    -124.19     41.78                15.0       3140.0           714.0   \n",
              "\n",
              "       population  households  median_income  median_house_value  \n",
              "0          1015.0       472.0         1.4936             66900.0  \n",
              "10         2434.0       824.0         2.6797             86500.0  \n",
              "14          949.0       300.0         0.8585             45000.0  \n",
              "23          137.0        52.0         3.2097             71300.0  \n",
              "32          481.0       185.0         6.2558            146300.0  \n",
              "...           ...         ...            ...                 ...  \n",
              "16871       830.0       279.0         3.2500            151400.0  \n",
              "16876      1177.0       414.0         3.2171            116200.0  \n",
              "16894       816.0       326.0         1.3190             70700.0  \n",
              "16957      1532.0       530.0         2.1829             69500.0  \n",
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              "\n",
              "[1051 rows x 9 columns]"
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              "    <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",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-114.31</td>\n",
              "      <td>34.19</td>\n",
              "      <td>15.0</td>\n",
              "      <td>5612.0</td>\n",
              "      <td>1283.0</td>\n",
              "      <td>1015.0</td>\n",
              "      <td>472.0</td>\n",
              "      <td>1.4936</td>\n",
              "      <td>66900.0</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>-114.60</td>\n",
              "      <td>33.62</td>\n",
              "      <td>16.0</td>\n",
              "      <td>3741.0</td>\n",
              "      <td>801.0</td>\n",
              "      <td>2434.0</td>\n",
              "      <td>824.0</td>\n",
              "      <td>2.6797</td>\n",
              "      <td>86500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>-114.63</td>\n",
              "      <td>32.76</td>\n",
              "      <td>15.0</td>\n",
              "      <td>1448.0</td>\n",
              "      <td>378.0</td>\n",
              "      <td>949.0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>0.8585</td>\n",
              "      <td>45000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>-114.98</td>\n",
              "      <td>33.82</td>\n",
              "      <td>15.0</td>\n",
              "      <td>644.0</td>\n",
              "      <td>129.0</td>\n",
              "      <td>137.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>3.2097</td>\n",
              "      <td>71300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>-115.39</td>\n",
              "      <td>32.76</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1136.0</td>\n",
              "      <td>196.0</td>\n",
              "      <td>481.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>6.2558</td>\n",
              "      <td>146300.0</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>16871</th>\n",
              "      <td>-123.75</td>\n",
              "      <td>39.37</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1377.0</td>\n",
              "      <td>296.0</td>\n",
              "      <td>830.0</td>\n",
              "      <td>279.0</td>\n",
              "      <td>3.2500</td>\n",
              "      <td>151400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16876</th>\n",
              "      <td>-123.79</td>\n",
              "      <td>39.44</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>423.0</td>\n",
              "      <td>1177.0</td>\n",
              "      <td>414.0</td>\n",
              "      <td>3.2171</td>\n",
              "      <td>116200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16894</th>\n",
              "      <td>-124.00</td>\n",
              "      <td>40.22</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2088.0</td>\n",
              "      <td>535.0</td>\n",
              "      <td>816.0</td>\n",
              "      <td>326.0</td>\n",
              "      <td>1.3190</td>\n",
              "      <td>70700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16957</th>\n",
              "      <td>-124.16</td>\n",
              "      <td>41.74</td>\n",
              "      <td>15.0</td>\n",
              "      <td>2715.0</td>\n",
              "      <td>569.0</td>\n",
              "      <td>1532.0</td>\n",
              "      <td>530.0</td>\n",
              "      <td>2.1829</td>\n",
              "      <td>69500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16983</th>\n",
              "      <td>-124.19</td>\n",
              "      <td>41.78</td>\n",
              "      <td>15.0</td>\n",
              "      <td>3140.0</td>\n",
              "      <td>714.0</td>\n",
              "      <td>1645.0</td>\n",
              "      <td>640.0</td>\n",
              "      <td>1.6654</td>\n",
              "      <td>74600.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1051 rows × 9 columns</p>\n",
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              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The second is isnull (and its companion notnull). These methods let you highlight values which are (or are not) empty (NaN). "
      ],
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      }
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        "housing_data.loc[housing_data.median_house_value.isnull()]"
      ],
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, median_house_value]\n",
              "Index: []"
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              "                                                     [key], {});\n",
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    {
      "cell_type": "code",
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        "housing_data.loc[housing_data.median_house_value.notnull()]\n"
      ],
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0        -114.31     34.19                15.0       5612.0          1283.0   \n",
              "1        -114.47     34.40                19.0       7650.0          1901.0   \n",
              "2        -114.56     33.69                17.0        720.0           174.0   \n",
              "3        -114.57     33.64                14.0       1501.0           337.0   \n",
              "4        -114.57     33.57                20.0       1454.0           326.0   \n",
              "...          ...       ...                 ...          ...             ...   \n",
              "16995    -124.26     40.58                52.0       2217.0           394.0   \n",
              "16996    -124.27     40.69                36.0       2349.0           528.0   \n",
              "16997    -124.30     41.84                17.0       2677.0           531.0   \n",
              "16998    -124.30     41.80                19.0       2672.0           552.0   \n",
              "16999    -124.35     40.54                52.0       1820.0           300.0   \n",
              "\n",
              "       population  households  median_income  median_house_value  \n",
              "0          1015.0       472.0         1.4936             66900.0  \n",
              "1          1129.0       463.0         1.8200             80100.0  \n",
              "2           333.0       117.0         1.6509             85700.0  \n",
              "3           515.0       226.0         3.1917             73400.0  \n",
              "4           624.0       262.0         1.9250             65500.0  \n",
              "...           ...         ...            ...                 ...  \n",
              "16995       907.0       369.0         2.3571            111400.0  \n",
              "16996      1194.0       465.0         2.5179             79000.0  \n",
              "16997      1244.0       456.0         3.0313            103600.0  \n",
              "16998      1298.0       478.0         1.9797             85800.0  \n",
              "16999       806.0       270.0         3.0147             94600.0  \n",
              "\n",
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              "      <td>66900.0</td>\n",
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              "      <th>1</th>\n",
              "      <td>-114.47</td>\n",
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              "      <th>2</th>\n",
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              "      <td>333.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>1.6509</td>\n",
              "      <td>85700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.64</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1501.0</td>\n",
              "      <td>337.0</td>\n",
              "      <td>515.0</td>\n",
              "      <td>226.0</td>\n",
              "      <td>3.1917</td>\n",
              "      <td>73400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.57</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1454.0</td>\n",
              "      <td>326.0</td>\n",
              "      <td>624.0</td>\n",
              "      <td>262.0</td>\n",
              "      <td>1.9250</td>\n",
              "      <td>65500.0</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>16995</th>\n",
              "      <td>-124.26</td>\n",
              "      <td>40.58</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2217.0</td>\n",
              "      <td>394.0</td>\n",
              "      <td>907.0</td>\n",
              "      <td>369.0</td>\n",
              "      <td>2.3571</td>\n",
              "      <td>111400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16996</th>\n",
              "      <td>-124.27</td>\n",
              "      <td>40.69</td>\n",
              "      <td>36.0</td>\n",
              "      <td>2349.0</td>\n",
              "      <td>528.0</td>\n",
              "      <td>1194.0</td>\n",
              "      <td>465.0</td>\n",
              "      <td>2.5179</td>\n",
              "      <td>79000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16997</th>\n",
              "      <td>-124.30</td>\n",
              "      <td>41.84</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2677.0</td>\n",
              "      <td>531.0</td>\n",
              "      <td>1244.0</td>\n",
              "      <td>456.0</td>\n",
              "      <td>3.0313</td>\n",
              "      <td>103600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16998</th>\n",
              "      <td>-124.30</td>\n",
              "      <td>41.80</td>\n",
              "      <td>19.0</td>\n",
              "      <td>2672.0</td>\n",
              "      <td>552.0</td>\n",
              "      <td>1298.0</td>\n",
              "      <td>478.0</td>\n",
              "      <td>1.9797</td>\n",
              "      <td>85800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16999</th>\n",
              "      <td>-124.35</td>\n",
              "      <td>40.54</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1820.0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>806.0</td>\n",
              "      <td>270.0</td>\n",
              "      <td>3.0147</td>\n",
              "      <td>94600.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17000 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-947aa4fd-1a12-4e85-8f88-f12a056cc12e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-947aa4fd-1a12-4e85-8f88-f12a056cc12e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-947aa4fd-1a12-4e85-8f88-f12a056cc12e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Assigning\n",
        "\n",
        "---\n",
        "---\n",
        "\n",
        "Going the other way, assigning data to a DataFrame is easy. You can assign either a constant value:"
      ],
      "metadata": {
        "id": "4vKMmGZ2o6Mz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data[\"condition\"] = \"Good\"\n",
        "housing_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "pxS-CKEPpFY9",
        "outputId": "89e1f378-c0c2-4a0b-b567-3143cf72af97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0        -114.31     34.19                15.0       5612.0          1283.0   \n",
              "1        -114.47     34.40                19.0       7650.0          1901.0   \n",
              "2        -114.56     33.69                17.0        720.0           174.0   \n",
              "3        -114.57     33.64                14.0       1501.0           337.0   \n",
              "4        -114.57     33.57                20.0       1454.0           326.0   \n",
              "...          ...       ...                 ...          ...             ...   \n",
              "16995    -124.26     40.58                52.0       2217.0           394.0   \n",
              "16996    -124.27     40.69                36.0       2349.0           528.0   \n",
              "16997    -124.30     41.84                17.0       2677.0           531.0   \n",
              "16998    -124.30     41.80                19.0       2672.0           552.0   \n",
              "16999    -124.35     40.54                52.0       1820.0           300.0   \n",
              "\n",
              "       population  households  median_income  median_house_value condition  \n",
              "0          1015.0       472.0         1.4936             66900.0      Good  \n",
              "1          1129.0       463.0         1.8200             80100.0      Good  \n",
              "2           333.0       117.0         1.6509             85700.0      Good  \n",
              "3           515.0       226.0         3.1917             73400.0      Good  \n",
              "4           624.0       262.0         1.9250             65500.0      Good  \n",
              "...           ...         ...            ...                 ...       ...  \n",
              "16995       907.0       369.0         2.3571            111400.0      Good  \n",
              "16996      1194.0       465.0         2.5179             79000.0      Good  \n",
              "16997      1244.0       456.0         3.0313            103600.0      Good  \n",
              "16998      1298.0       478.0         1.9797             85800.0      Good  \n",
              "16999       806.0       270.0         3.0147             94600.0      Good  \n",
              "\n",
              "[17000 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6ff32b07-9afb-46bb-b3c9-a172c9ecef79\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>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>condition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-114.31</td>\n",
              "      <td>34.19</td>\n",
              "      <td>15.0</td>\n",
              "      <td>5612.0</td>\n",
              "      <td>1283.0</td>\n",
              "      <td>1015.0</td>\n",
              "      <td>472.0</td>\n",
              "      <td>1.4936</td>\n",
              "      <td>66900.0</td>\n",
              "      <td>Good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-114.47</td>\n",
              "      <td>34.40</td>\n",
              "      <td>19.0</td>\n",
              "      <td>7650.0</td>\n",
              "      <td>1901.0</td>\n",
              "      <td>1129.0</td>\n",
              "      <td>463.0</td>\n",
              "      <td>1.8200</td>\n",
              "      <td>80100.0</td>\n",
              "      <td>Good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-114.56</td>\n",
              "      <td>33.69</td>\n",
              "      <td>17.0</td>\n",
              "      <td>720.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>333.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>1.6509</td>\n",
              "      <td>85700.0</td>\n",
              "      <td>Good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.64</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1501.0</td>\n",
              "      <td>337.0</td>\n",
              "      <td>515.0</td>\n",
              "      <td>226.0</td>\n",
              "      <td>3.1917</td>\n",
              "      <td>73400.0</td>\n",
              "      <td>Good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.57</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1454.0</td>\n",
              "      <td>326.0</td>\n",
              "      <td>624.0</td>\n",
              "      <td>262.0</td>\n",
              "      <td>1.9250</td>\n",
              "      <td>65500.0</td>\n",
              "      <td>Good</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16995</th>\n",
              "      <td>-124.26</td>\n",
              "      <td>40.58</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2217.0</td>\n",
              "      <td>394.0</td>\n",
              "      <td>907.0</td>\n",
              "      <td>369.0</td>\n",
              "      <td>2.3571</td>\n",
              "      <td>111400.0</td>\n",
              "      <td>Good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16996</th>\n",
              "      <td>-124.27</td>\n",
              "      <td>40.69</td>\n",
              "      <td>36.0</td>\n",
              "      <td>2349.0</td>\n",
              "      <td>528.0</td>\n",
              "      <td>1194.0</td>\n",
              "      <td>465.0</td>\n",
              "      <td>2.5179</td>\n",
              "      <td>79000.0</td>\n",
              "      <td>Good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16997</th>\n",
              "      <td>-124.30</td>\n",
              "      <td>41.84</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2677.0</td>\n",
              "      <td>531.0</td>\n",
              "      <td>1244.0</td>\n",
              "      <td>456.0</td>\n",
              "      <td>3.0313</td>\n",
              "      <td>103600.0</td>\n",
              "      <td>Good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16998</th>\n",
              "      <td>-124.30</td>\n",
              "      <td>41.80</td>\n",
              "      <td>19.0</td>\n",
              "      <td>2672.0</td>\n",
              "      <td>552.0</td>\n",
              "      <td>1298.0</td>\n",
              "      <td>478.0</td>\n",
              "      <td>1.9797</td>\n",
              "      <td>85800.0</td>\n",
              "      <td>Good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16999</th>\n",
              "      <td>-124.35</td>\n",
              "      <td>40.54</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1820.0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>806.0</td>\n",
              "      <td>270.0</td>\n",
              "      <td>3.0147</td>\n",
              "      <td>94600.0</td>\n",
              "      <td>Good</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17000 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6ff32b07-9afb-46bb-b3c9-a172c9ecef79')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6ff32b07-9afb-46bb-b3c9-a172c9ecef79 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6ff32b07-9afb-46bb-b3c9-a172c9ecef79');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Or with an iterable of values:"
      ],
      "metadata": {
        "id": "eWEFrl2CpZ2r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data[\"reversed_index\"] = range(len(housing_data),0,-1)\n",
        "housing_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "o4ZTsRQ2pVAg",
        "outputId": "8ce8bdab-70ce-451d-aadb-b0a3974e731f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0        -114.31     34.19                15.0       5612.0          1283.0   \n",
              "1        -114.47     34.40                19.0       7650.0          1901.0   \n",
              "2        -114.56     33.69                17.0        720.0           174.0   \n",
              "3        -114.57     33.64                14.0       1501.0           337.0   \n",
              "4        -114.57     33.57                20.0       1454.0           326.0   \n",
              "...          ...       ...                 ...          ...             ...   \n",
              "16995    -124.26     40.58                52.0       2217.0           394.0   \n",
              "16996    -124.27     40.69                36.0       2349.0           528.0   \n",
              "16997    -124.30     41.84                17.0       2677.0           531.0   \n",
              "16998    -124.30     41.80                19.0       2672.0           552.0   \n",
              "16999    -124.35     40.54                52.0       1820.0           300.0   \n",
              "\n",
              "       population  households  median_income  median_house_value condition  \\\n",
              "0          1015.0       472.0         1.4936             66900.0      Good   \n",
              "1          1129.0       463.0         1.8200             80100.0      Good   \n",
              "2           333.0       117.0         1.6509             85700.0      Good   \n",
              "3           515.0       226.0         3.1917             73400.0      Good   \n",
              "4           624.0       262.0         1.9250             65500.0      Good   \n",
              "...           ...         ...            ...                 ...       ...   \n",
              "16995       907.0       369.0         2.3571            111400.0      Good   \n",
              "16996      1194.0       465.0         2.5179             79000.0      Good   \n",
              "16997      1244.0       456.0         3.0313            103600.0      Good   \n",
              "16998      1298.0       478.0         1.9797             85800.0      Good   \n",
              "16999       806.0       270.0         3.0147             94600.0      Good   \n",
              "\n",
              "       reversed_index  \n",
              "0               17000  \n",
              "1               16999  \n",
              "2               16998  \n",
              "3               16997  \n",
              "4               16996  \n",
              "...               ...  \n",
              "16995               5  \n",
              "16996               4  \n",
              "16997               3  \n",
              "16998               2  \n",
              "16999               1  \n",
              "\n",
              "[17000 rows x 11 columns]"
            ],
            "text/html": [
              "\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",
              "      <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>condition</th>\n",
              "      <th>reversed_index</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-114.31</td>\n",
              "      <td>34.19</td>\n",
              "      <td>15.0</td>\n",
              "      <td>5612.0</td>\n",
              "      <td>1283.0</td>\n",
              "      <td>1015.0</td>\n",
              "      <td>472.0</td>\n",
              "      <td>1.4936</td>\n",
              "      <td>66900.0</td>\n",
              "      <td>Good</td>\n",
              "      <td>17000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-114.47</td>\n",
              "      <td>34.40</td>\n",
              "      <td>19.0</td>\n",
              "      <td>7650.0</td>\n",
              "      <td>1901.0</td>\n",
              "      <td>1129.0</td>\n",
              "      <td>463.0</td>\n",
              "      <td>1.8200</td>\n",
              "      <td>80100.0</td>\n",
              "      <td>Good</td>\n",
              "      <td>16999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-114.56</td>\n",
              "      <td>33.69</td>\n",
              "      <td>17.0</td>\n",
              "      <td>720.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>333.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>1.6509</td>\n",
              "      <td>85700.0</td>\n",
              "      <td>Good</td>\n",
              "      <td>16998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.64</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1501.0</td>\n",
              "      <td>337.0</td>\n",
              "      <td>515.0</td>\n",
              "      <td>226.0</td>\n",
              "      <td>3.1917</td>\n",
              "      <td>73400.0</td>\n",
              "      <td>Good</td>\n",
              "      <td>16997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.57</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1454.0</td>\n",
              "      <td>326.0</td>\n",
              "      <td>624.0</td>\n",
              "      <td>262.0</td>\n",
              "      <td>1.9250</td>\n",
              "      <td>65500.0</td>\n",
              "      <td>Good</td>\n",
              "      <td>16996</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16995</th>\n",
              "      <td>-124.26</td>\n",
              "      <td>40.58</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2217.0</td>\n",
              "      <td>394.0</td>\n",
              "      <td>907.0</td>\n",
              "      <td>369.0</td>\n",
              "      <td>2.3571</td>\n",
              "      <td>111400.0</td>\n",
              "      <td>Good</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16996</th>\n",
              "      <td>-124.27</td>\n",
              "      <td>40.69</td>\n",
              "      <td>36.0</td>\n",
              "      <td>2349.0</td>\n",
              "      <td>528.0</td>\n",
              "      <td>1194.0</td>\n",
              "      <td>465.0</td>\n",
              "      <td>2.5179</td>\n",
              "      <td>79000.0</td>\n",
              "      <td>Good</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16997</th>\n",
              "      <td>-124.30</td>\n",
              "      <td>41.84</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2677.0</td>\n",
              "      <td>531.0</td>\n",
              "      <td>1244.0</td>\n",
              "      <td>456.0</td>\n",
              "      <td>3.0313</td>\n",
              "      <td>103600.0</td>\n",
              "      <td>Good</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16998</th>\n",
              "      <td>-124.30</td>\n",
              "      <td>41.80</td>\n",
              "      <td>19.0</td>\n",
              "      <td>2672.0</td>\n",
              "      <td>552.0</td>\n",
              "      <td>1298.0</td>\n",
              "      <td>478.0</td>\n",
              "      <td>1.9797</td>\n",
              "      <td>85800.0</td>\n",
              "      <td>Good</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16999</th>\n",
              "      <td>-124.35</td>\n",
              "      <td>40.54</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1820.0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>806.0</td>\n",
              "      <td>270.0</td>\n",
              "      <td>3.0147</td>\n",
              "      <td>94600.0</td>\n",
              "      <td>Good</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17000 rows × 11 columns</p>\n",
              "</div>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Summary functions\n",
        "\n",
        "---\n",
        "---\n",
        "\n",
        "Pandas provides many simple \"summary functions\" (not an official name) which restructure the data in some useful way. For example, consider the **describe()** method:"
      ],
      "metadata": {
        "id": "CMSRCUutvyYY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.describe()     #for DataFrame     "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "mcBhjCMHv6IO",
        "outputId": "2ec883b2-b567-4878-fccd-e45d028748cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          longitude      latitude  housing_median_age   total_rooms  \\\n",
              "count  17000.000000  17000.000000        17000.000000  17000.000000   \n",
              "mean    -119.562108     35.625225           28.589353   2643.664412   \n",
              "std        2.005166      2.137340           12.586937   2179.947071   \n",
              "min     -124.350000     32.540000            1.000000      2.000000   \n",
              "25%     -121.790000     33.930000           18.000000   1462.000000   \n",
              "50%     -118.490000     34.250000           29.000000   2127.000000   \n",
              "75%     -118.000000     37.720000           37.000000   3151.250000   \n",
              "max     -114.310000     41.950000           52.000000  37937.000000   \n",
              "\n",
              "       total_bedrooms    population    households  median_income  \\\n",
              "count    17000.000000  17000.000000  17000.000000   17000.000000   \n",
              "mean       539.410824   1429.573941    501.221941       3.883578   \n",
              "std        421.499452   1147.852959    384.520841       1.908157   \n",
              "min          1.000000      3.000000      1.000000       0.499900   \n",
              "25%        297.000000    790.000000    282.000000       2.566375   \n",
              "50%        434.000000   1167.000000    409.000000       3.544600   \n",
              "75%        648.250000   1721.000000    605.250000       4.767000   \n",
              "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
              "\n",
              "       median_house_value  reversed_index  \n",
              "count        17000.000000    17000.000000  \n",
              "mean        207300.912353     8500.500000  \n",
              "std         115983.764387     4907.621624  \n",
              "min          14999.000000        1.000000  \n",
              "25%         119400.000000     4250.750000  \n",
              "50%         180400.000000     8500.500000  \n",
              "75%         265000.000000    12750.250000  \n",
              "max         500001.000000    17000.000000  "
            ],
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              "\n",
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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>reversed_index</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-119.562108</td>\n",
              "      <td>35.625225</td>\n",
              "      <td>28.589353</td>\n",
              "      <td>2643.664412</td>\n",
              "      <td>539.410824</td>\n",
              "      <td>1429.573941</td>\n",
              "      <td>501.221941</td>\n",
              "      <td>3.883578</td>\n",
              "      <td>207300.912353</td>\n",
              "      <td>8500.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.005166</td>\n",
              "      <td>2.137340</td>\n",
              "      <td>12.586937</td>\n",
              "      <td>2179.947071</td>\n",
              "      <td>421.499452</td>\n",
              "      <td>1147.852959</td>\n",
              "      <td>384.520841</td>\n",
              "      <td>1.908157</td>\n",
              "      <td>115983.764387</td>\n",
              "      <td>4907.621624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-124.350000</td>\n",
              "      <td>32.540000</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",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-121.790000</td>\n",
              "      <td>33.930000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1462.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>790.000000</td>\n",
              "      <td>282.000000</td>\n",
              "      <td>2.566375</td>\n",
              "      <td>119400.000000</td>\n",
              "      <td>4250.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-118.490000</td>\n",
              "      <td>34.250000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>2127.000000</td>\n",
              "      <td>434.000000</td>\n",
              "      <td>1167.000000</td>\n",
              "      <td>409.000000</td>\n",
              "      <td>3.544600</td>\n",
              "      <td>180400.000000</td>\n",
              "      <td>8500.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-118.000000</td>\n",
              "      <td>37.720000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>3151.250000</td>\n",
              "      <td>648.250000</td>\n",
              "      <td>1721.000000</td>\n",
              "      <td>605.250000</td>\n",
              "      <td>4.767000</td>\n",
              "      <td>265000.000000</td>\n",
              "      <td>12750.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>-114.310000</td>\n",
              "      <td>41.950000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>37937.000000</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",
              "      <td>17000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.condition.describe()    #for series (changes for data types too)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tn3fdJA1wUia",
        "outputId": "a469dc54-c558-4c7f-d735-7ba2cc9066ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     17000\n",
              "unique        1\n",
              "top        Good\n",
              "freq      17000\n",
              "Name: condition, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.latitude.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D0evJShDwqOr",
        "outputId": "539495ec-7f44-4749-bd75-4c8f537ed451"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    17000.000000\n",
              "mean        35.625225\n",
              "std          2.137340\n",
              "min         32.540000\n",
              "25%         33.930000\n",
              "50%         34.250000\n",
              "75%         37.720000\n",
              "max         41.950000\n",
              "Name: latitude, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mean of Series"
      ],
      "metadata": {
        "id": "bpTdnAeMxVxl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.latitude.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8gdtwJZFw2Uz",
        "outputId": "f8583bf2-5f79-499a-a0b8-7351bee2a25c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "35.62522470588235"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Unique Values in Series"
      ],
      "metadata": {
        "id": "zaS3G8V2xYqj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.housing_median_age.unique()        "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Au79_ewxENX",
        "outputId": "616638fd-0858-4cf0-f898-f7ad26f1d08c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([15., 19., 17., 14., 20., 29., 25., 41., 34., 46., 16., 21., 48.,\n",
              "       31., 28., 24., 18., 30., 32., 23., 38., 35., 33., 13., 11., 10.,\n",
              "        6., 12., 27., 22.,  5.,  8., 37.,  3., 44.,  2., 26.,  9.,  7.,\n",
              "       36.,  4., 52., 40., 43., 42., 39., 49., 47., 50., 45., 51.,  1.])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Unique Values and their counts as series of a Series"
      ],
      "metadata": {
        "id": "qeWq69fFxhli"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.housing_median_age.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mz0bcMhVxs4x",
        "outputId": "5d3db40d-23dc-4ae1-c7f2-bd5a1a0f3dae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "52.0    1052\n",
              "36.0     715\n",
              "35.0     692\n",
              "16.0     635\n",
              "17.0     576\n",
              "34.0     567\n",
              "33.0     513\n",
              "26.0     503\n",
              "18.0     478\n",
              "25.0     461\n",
              "32.0     458\n",
              "37.0     437\n",
              "15.0     416\n",
              "19.0     412\n",
              "28.0     400\n",
              "27.0     397\n",
              "24.0     389\n",
              "31.0     384\n",
              "30.0     384\n",
              "20.0     383\n",
              "23.0     382\n",
              "29.0     374\n",
              "21.0     373\n",
              "14.0     347\n",
              "22.0     323\n",
              "38.0     318\n",
              "42.0     308\n",
              "39.0     302\n",
              "44.0     296\n",
              "43.0     286\n",
              "13.0     249\n",
              "40.0     249\n",
              "45.0     235\n",
              "41.0     232\n",
              "10.0     226\n",
              "11.0     208\n",
              "5.0      199\n",
              "46.0     196\n",
              "12.0     192\n",
              "8.0      178\n",
              "47.0     175\n",
              "9.0      172\n",
              "4.0      161\n",
              "7.0      151\n",
              "48.0     135\n",
              "6.0      129\n",
              "50.0     112\n",
              "49.0     111\n",
              "2.0       49\n",
              "3.0       46\n",
              "51.0      32\n",
              "1.0        2\n",
              "Name: housing_median_age, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Maps\n",
        "\n",
        "\n",
        "---\n",
        "---\n",
        "A map is a term, borrowed from mathematics, for a function that takes one set of values and \"maps\" them to another set of values. In data science we often have a need for creating new representations from existing data, or for transforming data from the format it is in now to the format that we want it to be in later. Maps are what handle this work, making them extremely important for getting your work done!\n",
        "\n",
        "---\n",
        "\n",
        "There are two mapping methods that you will use often.\n",
        "\n",
        "**map()** is the first, and slightly simpler one. For example:"
      ],
      "metadata": {
        "id": "fL0uV1BLx9eh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data_housing_median_age_mean = housing_data.housing_median_age.mean()\n",
        "\n",
        "# Mapping data to get absolute deviation from mean...       Returns a mapped series, NOT AFFECTING MAIN DATA\n",
        "housing_data.housing_median_age.map(lambda p : p - housing_data_housing_median_age_mean)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kucx9zSXx4X1",
        "outputId": "2f3ce1fc-4031-459a-f19a-d83f4c16bca9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       -13.589353\n",
              "1        -9.589353\n",
              "2       -11.589353\n",
              "3       -14.589353\n",
              "4        -8.589353\n",
              "           ...    \n",
              "16995    23.410647\n",
              "16996     7.410647\n",
              "16997   -11.589353\n",
              "16998    -9.589353\n",
              "16999    23.410647\n",
              "Name: housing_median_age, Length: 17000, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The function you pass to map() should expect a single value from the Series (a point value, in the above example), and return a transformed version of that value. map() returns a new Series where all the values have been transformed by your function.\n",
        "\n",
        "**apply()** is the equivalent method if we want to transform a whole DataFrame by calling a custom method on each row."
      ],
      "metadata": {
        "id": "knRqS0WH0E4E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def remean_points(row):       ##Takes effect on each row\n",
        "    row.housing_median_age = row.housing_median_age - housing_data_housing_median_age_mean\n",
        "    return row\n",
        "\n",
        "# def conditional_show()\n",
        "# def modify_data()\n",
        "# def revise_data()\n",
        "# def rating(row):\n",
        "#     if:: return 5.......\n",
        "housing_data.apply(remean_points, axis='columns')         # Returns a mapped DataFrame, NOT AFFECTING MAIN DATA "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "KkJin8M8zTFF",
        "outputId": "894dc162-54db-475f-c17a-1366d266dbdd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0        -114.31     34.19          -13.589353       5612.0          1283.0   \n",
              "1        -114.47     34.40           -9.589353       7650.0          1901.0   \n",
              "2        -114.56     33.69          -11.589353        720.0           174.0   \n",
              "3        -114.57     33.64          -14.589353       1501.0           337.0   \n",
              "4        -114.57     33.57           -8.589353       1454.0           326.0   \n",
              "...          ...       ...                 ...          ...             ...   \n",
              "16995    -124.26     40.58           23.410647       2217.0           394.0   \n",
              "16996    -124.27     40.69            7.410647       2349.0           528.0   \n",
              "16997    -124.30     41.84          -11.589353       2677.0           531.0   \n",
              "16998    -124.30     41.80           -9.589353       2672.0           552.0   \n",
              "16999    -124.35     40.54           23.410647       1820.0           300.0   \n",
              "\n",
              "       population  households  median_income  median_house_value condition  \\\n",
              "0          1015.0       472.0         1.4936             66900.0      Good   \n",
              "1          1129.0       463.0         1.8200             80100.0      Good   \n",
              "2           333.0       117.0         1.6509             85700.0      Good   \n",
              "3           515.0       226.0         3.1917             73400.0      Good   \n",
              "4           624.0       262.0         1.9250             65500.0      Good   \n",
              "...           ...         ...            ...                 ...       ...   \n",
              "16995       907.0       369.0         2.3571            111400.0      Good   \n",
              "16996      1194.0       465.0         2.5179             79000.0      Good   \n",
              "16997      1244.0       456.0         3.0313            103600.0      Good   \n",
              "16998      1298.0       478.0         1.9797             85800.0      Good   \n",
              "16999       806.0       270.0         3.0147             94600.0      Good   \n",
              "\n",
              "       reversed_index  \n",
              "0               17000  \n",
              "1               16999  \n",
              "2               16998  \n",
              "3               16997  \n",
              "4               16996  \n",
              "...               ...  \n",
              "16995               5  \n",
              "16996               4  \n",
              "16997               3  \n",
              "16998               2  \n",
              "16999               1  \n",
              "\n",
              "[17000 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f3797375-7235-4421-b030-c87d4311ca23\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>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>condition</th>\n",
              "      <th>reversed_index</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-114.31</td>\n",
              "      <td>34.19</td>\n",
              "      <td>-13.589353</td>\n",
              "      <td>5612.0</td>\n",
              "      <td>1283.0</td>\n",
              "      <td>1015.0</td>\n",
              "      <td>472.0</td>\n",
              "      <td>1.4936</td>\n",
              "      <td>66900.0</td>\n",
              "      <td>Good</td>\n",
              "      <td>17000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-114.47</td>\n",
              "      <td>34.40</td>\n",
              "      <td>-9.589353</td>\n",
              "      <td>7650.0</td>\n",
              "      <td>1901.0</td>\n",
              "      <td>1129.0</td>\n",
              "      <td>463.0</td>\n",
              "      <td>1.8200</td>\n",
              "      <td>80100.0</td>\n",
              "      <td>Good</td>\n",
              "      <td>16999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-114.56</td>\n",
              "      <td>33.69</td>\n",
              "      <td>-11.589353</td>\n",
              "      <td>720.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>333.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>1.6509</td>\n",
              "      <td>85700.0</td>\n",
              "      <td>Good</td>\n",
              "      <td>16998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.64</td>\n",
              "      <td>-14.589353</td>\n",
              "      <td>1501.0</td>\n",
              "      <td>337.0</td>\n",
              "      <td>515.0</td>\n",
              "      <td>226.0</td>\n",
              "      <td>3.1917</td>\n",
              "      <td>73400.0</td>\n",
              "      <td>Good</td>\n",
              "      <td>16997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.57</td>\n",
              "      <td>-8.589353</td>\n",
              "      <td>1454.0</td>\n",
              "      <td>326.0</td>\n",
              "      <td>624.0</td>\n",
              "      <td>262.0</td>\n",
              "      <td>1.9250</td>\n",
              "      <td>65500.0</td>\n",
              "      <td>Good</td>\n",
              "      <td>16996</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16995</th>\n",
              "      <td>-124.26</td>\n",
              "      <td>40.58</td>\n",
              "      <td>23.410647</td>\n",
              "      <td>2217.0</td>\n",
              "      <td>394.0</td>\n",
              "      <td>907.0</td>\n",
              "      <td>369.0</td>\n",
              "      <td>2.3571</td>\n",
              "      <td>111400.0</td>\n",
              "      <td>Good</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16996</th>\n",
              "      <td>-124.27</td>\n",
              "      <td>40.69</td>\n",
              "      <td>7.410647</td>\n",
              "      <td>2349.0</td>\n",
              "      <td>528.0</td>\n",
              "      <td>1194.0</td>\n",
              "      <td>465.0</td>\n",
              "      <td>2.5179</td>\n",
              "      <td>79000.0</td>\n",
              "      <td>Good</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16997</th>\n",
              "      <td>-124.30</td>\n",
              "      <td>41.84</td>\n",
              "      <td>-11.589353</td>\n",
              "      <td>2677.0</td>\n",
              "      <td>531.0</td>\n",
              "      <td>1244.0</td>\n",
              "      <td>456.0</td>\n",
              "      <td>3.0313</td>\n",
              "      <td>103600.0</td>\n",
              "      <td>Good</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16998</th>\n",
              "      <td>-124.30</td>\n",
              "      <td>41.80</td>\n",
              "      <td>-9.589353</td>\n",
              "      <td>2672.0</td>\n",
              "      <td>552.0</td>\n",
              "      <td>1298.0</td>\n",
              "      <td>478.0</td>\n",
              "      <td>1.9797</td>\n",
              "      <td>85800.0</td>\n",
              "      <td>Good</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16999</th>\n",
              "      <td>-124.35</td>\n",
              "      <td>40.54</td>\n",
              "      <td>23.410647</td>\n",
              "      <td>1820.0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>806.0</td>\n",
              "      <td>270.0</td>\n",
              "      <td>3.0147</td>\n",
              "      <td>94600.0</td>\n",
              "      <td>Good</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17000 rows × 11 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f3797375-7235-4421-b030-c87d4311ca23')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "          document.querySelector('#df-f3797375-7235-4421-b030-c87d4311ca23 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f3797375-7235-4421-b030-c87d4311ca23');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "**Basic Operators** works faster and in a same fashion as map() and apply() in pandas.\n",
        "\n",
        "These operators are faster than map() or apply() because they use speed ups built into pandas. All of the standard Python operators (>, <, ==, and so on) work in this manner.\n"
      ],
      "metadata": {
        "id": "IZAbzbf81x2c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.housing_median_age - housing_data_housing_median_age_mean"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wv9dsbsS2SOr",
        "outputId": "4a530ff5-56b5-4f39-f4e3-8e26391ee969"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       -13.589353\n",
              "1        -9.589353\n",
              "2       -11.589353\n",
              "3       -14.589353\n",
              "4        -8.589353\n",
              "           ...    \n",
              "16995    23.410647\n",
              "16996     7.410647\n",
              "16997   -11.589353\n",
              "16998    -9.589353\n",
              "16999    23.410647\n",
              "Name: housing_median_age, Length: 17000, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.latitude.astype(str) + \"\\u00b0 N \" + housing_data.longitude.astype(str) + \"\\u00b0 E\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s5c0V9vs2bsG",
        "outputId": "a8317410-e5e7-4a90-ed24-0aa8d5d8d86b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        34.19° N -114.31° E\n",
              "1         34.4° N -114.47° E\n",
              "2        33.69° N -114.56° E\n",
              "3        33.64° N -114.57° E\n",
              "4        33.57° N -114.57° E\n",
              "                ...         \n",
              "16995    40.58° N -124.26° E\n",
              "16996    40.69° N -124.27° E\n",
              "16997     41.84° N -124.3° E\n",
              "16998      41.8° N -124.3° E\n",
              "16999    40.54° N -124.35° E\n",
              "Length: 17000, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Groupwise analysis\n",
        "\n",
        "---\n",
        "---\n",
        "\n",
        "refer to :: https://pandas.pydata.org/docs/reference/groupby.html"
      ],
      "metadata": {
        "id": "12Ola9mMLhIf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data = pd.read_csv(\"sample_data/california_housing_train.csv\")\n",
        "housing_data.groupby(\"housing_median_age\").describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "UElDSsjzP5jW",
        "outputId": "8d606e7f-eae0-409c-a0eb-059be805eb6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   longitude                                                   \\\n",
              "                       count        mean       std     min       25%      50%   \n",
              "housing_median_age                                                              \n",
              "1.0                      2.0 -121.465000  0.756604 -122.00 -121.7325 -121.465   \n",
              "2.0                     49.0 -119.035306  2.086532 -122.51 -121.3500 -117.760   \n",
              "3.0                     46.0 -118.798478  1.980720 -122.33 -121.0475 -117.780   \n",
              "4.0                    161.0 -118.805093  1.847526 -122.72 -120.4600 -117.760   \n",
              "5.0                    199.0 -118.789497  1.937639 -122.55 -121.0300 -117.890   \n",
              "6.0                    129.0 -118.915969  1.893689 -122.76 -121.0300 -118.160   \n",
              "7.0                    151.0 -119.165497  1.982453 -122.79 -121.2600 -118.460   \n",
              "8.0                    178.0 -119.375056  1.969668 -123.00 -121.2525 -119.215   \n",
              "9.0                    172.0 -119.324186  1.932799 -123.49 -121.1350 -119.020   \n",
              "10.0                   226.0 -119.350841  1.966870 -123.40 -121.2575 -119.045   \n",
              "11.0                   208.0 -119.517837  2.008043 -124.23 -121.3125 -119.665   \n",
              "12.0                   192.0 -119.405000  2.029160 -124.09 -121.3400 -119.150   \n",
              "13.0                   249.0 -119.406586  1.959151 -124.17 -121.2400 -119.120   \n",
              "14.0                   347.0 -119.510605  2.045823 -124.05 -121.3350 -119.560   \n",
              "15.0                   416.0 -119.471490  2.126938 -124.19 -121.5050 -119.280   \n",
              "16.0                   635.0 -119.530094  2.094814 -124.00 -121.7550 -119.080   \n",
              "17.0                   576.0 -119.597656  2.143916 -124.30 -121.7250 -119.315   \n",
              "18.0                   478.0 -119.640209  2.076978 -124.10 -121.7200 -119.305   \n",
              "19.0                   412.0 -119.541820  2.042815 -124.30 -121.5800 -119.190   \n",
              "20.0                   383.0 -119.862350  2.132411 -124.21 -121.8900 -119.760   \n",
              "21.0                   373.0 -119.560134  2.023540 -124.19 -121.6000 -119.080   \n",
              "22.0                   323.0 -119.598266  1.974017 -124.14 -121.6350 -119.180   \n",
              "23.0                   382.0 -119.590890  2.076365 -124.16 -121.7600 -118.945   \n",
              "24.0                   389.0 -119.344910  1.925299 -124.15 -121.4100 -118.460   \n",
              "25.0                   461.0 -119.426898  1.937591 -124.14 -121.5100 -118.460   \n",
              "26.0                   503.0 -119.477654  1.971470 -124.17 -121.7650 -118.500   \n",
              "27.0                   397.0 -119.708363  1.957695 -124.14 -121.7600 -119.050   \n",
              "28.0                   400.0 -119.568050  2.004950 -124.22 -121.8400 -118.490   \n",
              "29.0                   374.0 -119.360348  1.954722 -124.14 -121.4475 -118.440   \n",
              "30.0                   384.0 -119.499922  1.933400 -124.19 -121.6700 -118.430   \n",
              "31.0                   384.0 -119.412865  1.962201 -124.13 -121.4400 -118.420   \n",
              "32.0                   458.0 -119.465917  1.933148 -124.25 -121.4975 -118.425   \n",
              "33.0                   513.0 -119.394211  1.953711 -124.18 -121.5100 -118.390   \n",
              "34.0                   567.0 -119.195714  1.838514 -124.18 -121.2750 -118.350   \n",
              "35.0                   692.0 -119.233786  1.823856 -124.18 -121.2625 -118.350   \n",
              "36.0                   715.0 -119.232434  1.775785 -124.27 -121.3050 -118.310   \n",
              "37.0                   437.0 -119.464371  1.949705 -124.19 -121.7700 -118.340   \n",
              "38.0                   318.0 -119.334245  1.918090 -124.14 -121.4200 -118.360   \n",
              "39.0                   302.0 -119.621556  1.908547 -124.18 -121.9175 -118.395   \n",
              "40.0                   249.0 -119.576386  1.851377 -122.82 -121.6200 -118.370   \n",
              "41.0                   232.0 -119.390086  1.785995 -123.98 -121.3825 -118.390   \n",
              "42.0                   308.0 -119.480292  1.827079 -124.10 -121.4900 -118.355   \n",
              "43.0                   286.0 -119.689301  1.913316 -124.17 -121.8675 -118.410   \n",
              "44.0                   296.0 -119.460034  1.865856 -124.09 -121.6500 -118.340   \n",
              "45.0                   235.0 -119.668596  1.913955 -123.22 -122.1450 -118.380   \n",
              "46.0                   196.0 -119.877755  2.030780 -124.16 -122.1625 -118.470   \n",
              "47.0                   175.0 -119.985029  1.969042 -124.15 -122.1700 -118.480   \n",
              "48.0                   135.0 -119.837556  1.993466 -122.97 -122.1650 -118.450   \n",
              "49.0                   111.0 -120.374324  2.047430 -123.79 -122.2800 -121.230   \n",
              "50.0                   112.0 -120.077143  2.054972 -124.16 -122.2225 -119.505   \n",
              "51.0                    32.0 -120.593750  2.086961 -122.48 -122.2850 -122.075   \n",
              "52.0                  1052.0 -120.917253  1.961408 -124.35 -122.4100 -122.220   \n",
              "\n",
              "                                     latitude             ... median_income  \\\n",
              "                         75%     max    count       mean  ...           75%   \n",
              "housing_median_age                                        ...                 \n",
              "1.0                -121.1975 -120.93      2.0  37.940000  ...      5.010200   \n",
              "2.0                -117.3300 -115.80     49.0  35.410816  ...      6.111200   \n",
              "3.0                -117.2550 -115.60     46.0  35.164783  ...      6.128600   \n",
              "4.0                -117.5400 -116.76    161.0  34.987764  ...      6.072200   \n",
              "5.0                -117.2300 -115.55    199.0  35.095327  ...      5.702000   \n",
              "6.0                -117.3200 -115.52    129.0  35.183798  ...      5.237500   \n",
              "7.0                -117.2950 -116.31    151.0  35.627616  ...      5.074650   \n",
              "8.0                -117.5050 -115.59    178.0  35.785899  ...      5.324325   \n",
              "9.0                -117.5625 -116.22    172.0  35.654942  ...      5.437150   \n",
              "10.0               -117.6000 -115.52    226.0  35.881681  ...      5.181925   \n",
              "11.0               -117.6325 -115.51    208.0  35.930288  ...      4.657175   \n",
              "12.0               -117.4950 -115.53    192.0  35.806458  ...      4.963500   \n",
              "13.0               -117.6900 -115.50    249.0  35.814819  ...      5.038900   \n",
              "14.0               -117.6250 -114.57    347.0  35.905159  ...      5.041800   \n",
              "15.0               -117.6475 -114.31    416.0  35.878341  ...      5.089475   \n",
              "16.0               -117.6650 -114.60    635.0  35.685748  ...      5.446950   \n",
              "17.0               -117.7275 -114.56    576.0  35.907882  ...      5.025550   \n",
              "18.0               -117.8825 -115.22    478.0  35.938054  ...      5.093075   \n",
              "19.0               -117.8600 -114.47    412.0  35.842597  ...      4.782175   \n",
              "20.0               -118.0150 -114.57    383.0  36.136449  ...      4.632800   \n",
              "21.0               -117.8900 -114.60    373.0  35.751635  ...      4.650000   \n",
              "22.0               -117.9650 -115.55    323.0  35.772879  ...      4.734400   \n",
              "23.0               -117.8825 -115.37    382.0  35.718115  ...      5.077525   \n",
              "24.0               -117.9000 -114.73    389.0  35.391054  ...      5.000000   \n",
              "25.0               -117.9600 -114.58    461.0  35.470108  ...      5.285200   \n",
              "26.0               -117.9150 -116.00    503.0  35.507038  ...      5.267450   \n",
              "27.0               -118.0300 -115.54    397.0  35.707128  ...      4.711000   \n",
              "28.0               -117.9800 -114.65    400.0  35.571075  ...      4.644000   \n",
              "29.0               -117.9600 -114.58    374.0  35.427968  ...      4.375000   \n",
              "30.0               -118.0400 -115.37    384.0  35.476120  ...      4.462775   \n",
              "31.0               -118.0175 -114.61    384.0  35.370990  ...      4.309400   \n",
              "32.0               -118.0225 -115.37    458.0  35.472664  ...      4.464600   \n",
              "33.0               -117.9500 -115.46    513.0  35.402378  ...      4.660700   \n",
              "34.0               -118.0050 -114.59    567.0  35.174603  ...      4.756650   \n",
              "35.0               -118.0100 -115.38    692.0  35.163627  ...      4.838775   \n",
              "36.0               -118.1000 -116.56    715.0  35.127972  ...      4.775250   \n",
              "37.0               -118.1100 -115.60    437.0  35.335378  ...      4.419600   \n",
              "38.0               -118.1200 -115.38    318.0  35.325472  ...      4.128450   \n",
              "39.0               -118.1900 -117.07    302.0  35.513146  ...      4.121875   \n",
              "40.0               -118.1900 -116.98    249.0  35.466265  ...      4.044600   \n",
              "41.0               -118.2000 -114.59    232.0  35.316983  ...      3.911450   \n",
              "42.0               -118.1900 -117.07    308.0  35.361169  ...      4.013025   \n",
              "43.0               -118.2100 -117.01    286.0  35.582587  ...      4.019875   \n",
              "44.0               -118.1800 -115.73    296.0  35.276351  ...      4.314225   \n",
              "45.0               -118.1850 -117.12    235.0  35.468979  ...      4.546200   \n",
              "46.0               -118.2600 -114.60    196.0  35.732194  ...      3.981700   \n",
              "47.0               -118.2700 -117.10    175.0  35.843943  ...      3.995200   \n",
              "48.0               -118.2850 -114.61    135.0  35.730222  ...      4.002500   \n",
              "49.0               -118.2900 -117.10    111.0  36.097748  ...      4.262500   \n",
              "50.0               -118.2700 -117.11    112.0  35.900893  ...      3.847775   \n",
              "51.0               -118.2925 -117.15     32.0  36.325937  ...      4.381250   \n",
              "52.0               -118.3500 -116.88   1052.0  36.634230  ...      4.540625   \n",
              "\n",
              "                            median_house_value                                \\\n",
              "                        max              count           mean            std   \n",
              "housing_median_age                                                             \n",
              "1.0                  5.2636                2.0  190250.000000    1484.924240   \n",
              "2.0                 10.1531               49.0  229438.836735  118029.406620   \n",
              "3.0                 11.5199               46.0  239450.043478  108822.178826   \n",
              "4.0                 13.4883              161.0  230054.105590  107317.502612   \n",
              "5.0                 12.6320              199.0  211035.708543  109779.562049   \n",
              "6.0                 13.0502              129.0  206768.240310  104227.462308   \n",
              "7.0                 13.8093              151.0  188445.059603   97741.450307   \n",
              "8.0                 14.9009              178.0  190805.073034   98183.414798   \n",
              "9.0                 10.8289              172.0  190306.994186   97492.006264   \n",
              "10.0                12.5420              226.0  178416.393805   96205.687641   \n",
              "11.0                13.1477              208.0  182480.312500   97931.083043   \n",
              "12.0                10.0760              192.0  181590.640625   94345.428409   \n",
              "13.0                 9.8346              249.0  188065.473896   94188.280352   \n",
              "14.0                15.0001              347.0  191181.288184   96986.243165   \n",
              "15.0                15.0001              416.0  181031.024038   94908.428125   \n",
              "16.0                12.0088              635.0  200354.507087   98994.507672   \n",
              "17.0                15.0001              576.0  191772.244792  101836.425101   \n",
              "18.0                11.6017              478.0  192074.715481  107590.164086   \n",
              "19.0                15.0000              412.0  196017.752427  110465.017772   \n",
              "20.0                12.2092              383.0  192681.751958  111869.959953   \n",
              "21.0                11.6833              373.0  200918.536193  115759.422811   \n",
              "22.0                11.2463              323.0  213129.173375  121153.981159   \n",
              "23.0                12.9792              382.0  204621.787958  115965.729813   \n",
              "24.0                14.5833              389.0  207293.365039  111247.078793   \n",
              "25.0                13.3913              461.0  223097.223427  118750.787480   \n",
              "26.0                15.0001              503.0  206773.793241  112092.926462   \n",
              "27.0                15.0001              397.0  206728.259446  114236.712734   \n",
              "28.0                14.4113              400.0  210049.812500  119811.441221   \n",
              "29.0                13.9470              374.0  195185.863636  117739.536913   \n",
              "30.0                12.4429              384.0  201559.145833  112936.213780   \n",
              "31.0                15.0001              384.0  208319.851562  120693.635055   \n",
              "32.0                15.0001              458.0  207131.500000  121348.298305   \n",
              "33.0                15.0001              513.0  203922.269006  114256.560418   \n",
              "34.0                15.0001              567.0  213061.615520  122656.145392   \n",
              "35.0                15.0001              692.0  205624.462428  108101.766134   \n",
              "36.0                15.0001              715.0  208262.422378  106735.813804   \n",
              "37.0                13.3670              437.0  209008.299771  119736.766750   \n",
              "38.0                12.7823              318.0  195272.371069  120893.707372   \n",
              "39.0                11.8603              302.0  206718.913907  125389.522805   \n",
              "40.0                15.0001              249.0  205783.594378  123428.327920   \n",
              "41.0                 8.8619              232.0  197328.935345  125630.370118   \n",
              "42.0                15.0001              308.0  199635.110390  112547.856076   \n",
              "43.0                15.0001              286.0  194365.779720  116726.685274   \n",
              "44.0                12.4237              296.0  214909.506757  124080.375756   \n",
              "45.0                15.0001              235.0  223464.744681  116254.957305   \n",
              "46.0                15.0001              196.0  198036.260204  109881.865243   \n",
              "47.0                 9.4590              175.0  190064.611429  110788.506693   \n",
              "48.0                15.0001              135.0  224665.251852  138057.219438   \n",
              "49.0                12.9591              111.0  220667.612613  115402.562465   \n",
              "50.0                13.3036              112.0  222513.482143  131205.503471   \n",
              "51.0                 9.3694               32.0  244850.125000  140659.405750   \n",
              "52.0                15.0001             1052.0  277368.200570  139743.309230   \n",
              "\n",
              "                                                                      \n",
              "                         min       25%       50%       75%       max  \n",
              "housing_median_age                                                    \n",
              "1.0                 189200.0  189725.0  190250.0  190775.0  191300.0  \n",
              "2.0                  47500.0  146300.0  191700.0  279000.0  500001.0  \n",
              "3.0                  83200.0  159225.0  216400.0  283225.0  500001.0  \n",
              "4.0                  42500.0  149200.0  198900.0  282500.0  500001.0  \n",
              "5.0                  50000.0  132350.0  176900.0  263750.0  500001.0  \n",
              "6.0                  58000.0  131000.0  177500.0  258100.0  500001.0  \n",
              "7.0                  56200.0  121950.0  162200.0  219950.0  500001.0  \n",
              "8.0                  22500.0  120600.0  162500.0  248775.0  500001.0  \n",
              "9.0                  54200.0  124775.0  162600.0  227825.0  500001.0  \n",
              "10.0                 53300.0  112625.0  152100.0  220100.0  500001.0  \n",
              "11.0                 50400.0  114450.0  160250.0  225675.0  500001.0  \n",
              "12.0                 40000.0  112575.0  156700.0  228075.0  500001.0  \n",
              "13.0                 43000.0  116100.0  162500.0  237900.0  500001.0  \n",
              "14.0                 32900.0  121050.0  170100.0  240350.0  500001.0  \n",
              "15.0                 32500.0  111550.0  161300.0  228300.0  500001.0  \n",
              "16.0                 14999.0  128300.0  179500.0  261250.0  500001.0  \n",
              "17.0                 27500.0  117375.0  166450.0  242175.0  500001.0  \n",
              "18.0                 47500.0  109175.0  166200.0  255000.0  500001.0  \n",
              "19.0                 14999.0  111900.0  173450.0  255250.0  500001.0  \n",
              "20.0                 32500.0  105100.0  164600.0  247550.0  500001.0  \n",
              "21.0                 25000.0  111500.0  174100.0  268100.0  500001.0  \n",
              "22.0                 34400.0  120800.0  186900.0  276350.0  500001.0  \n",
              "23.0                 30000.0  112800.0  184550.0  259250.0  500001.0  \n",
              "24.0                 32500.0  118800.0  189500.0  265200.0  500001.0  \n",
              "25.0                 45500.0  132700.0  209900.0  278100.0  500001.0  \n",
              "26.0                 36600.0  122150.0  188000.0  260800.0  500001.0  \n",
              "27.0                 40000.0  118800.0  184200.0  267700.0  500001.0  \n",
              "28.0                 40900.0  119600.0  187450.0  269075.0  500001.0  \n",
              "29.0                 28300.0  102275.0  169550.0  248700.0  500001.0  \n",
              "30.0                 42500.0  120475.0  171550.0  254050.0  500001.0  \n",
              "31.0                 34200.0  120350.0  183850.0  267225.0  500001.0  \n",
              "32.0                 43100.0  113725.0  181600.0  260850.0  500001.0  \n",
              "33.0                 22500.0  125000.0  175000.0  255600.0  500001.0  \n",
              "34.0                 26600.0  125250.0  185800.0  258050.0  500001.0  \n",
              "35.0                 39600.0  133850.0  184250.0  254750.0  500001.0  \n",
              "36.0                 14999.0  142050.0  189700.0  248900.0  500001.0  \n",
              "37.0                 42100.0  118300.0  175900.0  267500.0  500001.0  \n",
              "38.0                 39400.0  106375.0  161600.0  248175.0  500001.0  \n",
              "39.0                 17500.0  108750.0  171500.0  273250.0  500001.0  \n",
              "40.0                 43000.0  112500.0  172200.0  247300.0  500001.0  \n",
              "41.0                 41800.0  100675.0  160800.0  247625.0  500001.0  \n",
              "42.0                 39400.0  120000.0  168350.0  242600.0  500001.0  \n",
              "43.0                 46600.0  105925.0  162500.0  253150.0  500001.0  \n",
              "44.0                 48300.0  112500.0  178750.0  295225.0  500001.0  \n",
              "45.0                 30000.0  140050.0  198400.0  280150.0  500001.0  \n",
              "46.0                 26900.0  110550.0  174250.0  246925.0  500001.0  \n",
              "47.0                 49600.0  108850.0  162900.0  235800.0  500001.0  \n",
              "48.0                 48600.0  104350.0  189700.0  324650.0  500001.0  \n",
              "49.0                 52000.0  118700.0  217300.0  283350.0  500001.0  \n",
              "50.0                 54200.0  125000.0  176650.0  316550.0  500001.0  \n",
              "51.0                 51900.0  123775.0  224550.0  322275.0  500001.0  \n",
              "52.0                 14999.0  162500.0  261200.0  386775.0  500001.0  \n",
              "\n",
              "[52 rows x 64 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5ab43611-9244-4d6b-a46b-1866694f1441\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"8\" halign=\"left\">longitude</th>\n",
              "      <th colspan=\"2\" halign=\"left\">latitude</th>\n",
              "      <th>...</th>\n",
              "      <th colspan=\"2\" halign=\"left\">median_income</th>\n",
              "      <th colspan=\"8\" halign=\"left\">median_house_value</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>...</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>housing_median_age</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",
              "      <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",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>2.0</td>\n",
              "      <td>-121.465000</td>\n",
              "      <td>0.756604</td>\n",
              "      <td>-122.00</td>\n",
              "      <td>-121.7325</td>\n",
              "      <td>-121.465</td>\n",
              "      <td>-121.1975</td>\n",
              "      <td>-120.93</td>\n",
              "      <td>2.0</td>\n",
              "      <td>37.940000</td>\n",
              "      <td>...</td>\n",
              "      <td>5.010200</td>\n",
              "      <td>5.2636</td>\n",
              "      <td>2.0</td>\n",
              "      <td>190250.000000</td>\n",
              "      <td>1484.924240</td>\n",
              "      <td>189200.0</td>\n",
              "      <td>189725.0</td>\n",
              "      <td>190250.0</td>\n",
              "      <td>190775.0</td>\n",
              "      <td>191300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>49.0</td>\n",
              "      <td>-119.035306</td>\n",
              "      <td>2.086532</td>\n",
              "      <td>-122.51</td>\n",
              "      <td>-121.3500</td>\n",
              "      <td>-117.760</td>\n",
              "      <td>-117.3300</td>\n",
              "      <td>-115.80</td>\n",
              "      <td>49.0</td>\n",
              "      <td>35.410816</td>\n",
              "      <td>...</td>\n",
              "      <td>6.111200</td>\n",
              "      <td>10.1531</td>\n",
              "      <td>49.0</td>\n",
              "      <td>229438.836735</td>\n",
              "      <td>118029.406620</td>\n",
              "      <td>47500.0</td>\n",
              "      <td>146300.0</td>\n",
              "      <td>191700.0</td>\n",
              "      <td>279000.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <td>46.0</td>\n",
              "      <td>-118.798478</td>\n",
              "      <td>1.980720</td>\n",
              "      <td>-122.33</td>\n",
              "      <td>-121.0475</td>\n",
              "      <td>-117.780</td>\n",
              "      <td>-117.2550</td>\n",
              "      <td>-115.60</td>\n",
              "      <td>46.0</td>\n",
              "      <td>35.164783</td>\n",
              "      <td>...</td>\n",
              "      <td>6.128600</td>\n",
              "      <td>11.5199</td>\n",
              "      <td>46.0</td>\n",
              "      <td>239450.043478</td>\n",
              "      <td>108822.178826</td>\n",
              "      <td>83200.0</td>\n",
              "      <td>159225.0</td>\n",
              "      <td>216400.0</td>\n",
              "      <td>283225.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.0</th>\n",
              "      <td>161.0</td>\n",
              "      <td>-118.805093</td>\n",
              "      <td>1.847526</td>\n",
              "      <td>-122.72</td>\n",
              "      <td>-120.4600</td>\n",
              "      <td>-117.760</td>\n",
              "      <td>-117.5400</td>\n",
              "      <td>-116.76</td>\n",
              "      <td>161.0</td>\n",
              "      <td>34.987764</td>\n",
              "      <td>...</td>\n",
              "      <td>6.072200</td>\n",
              "      <td>13.4883</td>\n",
              "      <td>161.0</td>\n",
              "      <td>230054.105590</td>\n",
              "      <td>107317.502612</td>\n",
              "      <td>42500.0</td>\n",
              "      <td>149200.0</td>\n",
              "      <td>198900.0</td>\n",
              "      <td>282500.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.0</th>\n",
              "      <td>199.0</td>\n",
              "      <td>-118.789497</td>\n",
              "      <td>1.937639</td>\n",
              "      <td>-122.55</td>\n",
              "      <td>-121.0300</td>\n",
              "      <td>-117.890</td>\n",
              "      <td>-117.2300</td>\n",
              "      <td>-115.55</td>\n",
              "      <td>199.0</td>\n",
              "      <td>35.095327</td>\n",
              "      <td>...</td>\n",
              "      <td>5.702000</td>\n",
              "      <td>12.6320</td>\n",
              "      <td>199.0</td>\n",
              "      <td>211035.708543</td>\n",
              "      <td>109779.562049</td>\n",
              "      <td>50000.0</td>\n",
              "      <td>132350.0</td>\n",
              "      <td>176900.0</td>\n",
              "      <td>263750.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6.0</th>\n",
              "      <td>129.0</td>\n",
              "      <td>-118.915969</td>\n",
              "      <td>1.893689</td>\n",
              "      <td>-122.76</td>\n",
              "      <td>-121.0300</td>\n",
              "      <td>-118.160</td>\n",
              "      <td>-117.3200</td>\n",
              "      <td>-115.52</td>\n",
              "      <td>129.0</td>\n",
              "      <td>35.183798</td>\n",
              "      <td>...</td>\n",
              "      <td>5.237500</td>\n",
              "      <td>13.0502</td>\n",
              "      <td>129.0</td>\n",
              "      <td>206768.240310</td>\n",
              "      <td>104227.462308</td>\n",
              "      <td>58000.0</td>\n",
              "      <td>131000.0</td>\n",
              "      <td>177500.0</td>\n",
              "      <td>258100.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7.0</th>\n",
              "      <td>151.0</td>\n",
              "      <td>-119.165497</td>\n",
              "      <td>1.982453</td>\n",
              "      <td>-122.79</td>\n",
              "      <td>-121.2600</td>\n",
              "      <td>-118.460</td>\n",
              "      <td>-117.2950</td>\n",
              "      <td>-116.31</td>\n",
              "      <td>151.0</td>\n",
              "      <td>35.627616</td>\n",
              "      <td>...</td>\n",
              "      <td>5.074650</td>\n",
              "      <td>13.8093</td>\n",
              "      <td>151.0</td>\n",
              "      <td>188445.059603</td>\n",
              "      <td>97741.450307</td>\n",
              "      <td>56200.0</td>\n",
              "      <td>121950.0</td>\n",
              "      <td>162200.0</td>\n",
              "      <td>219950.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8.0</th>\n",
              "      <td>178.0</td>\n",
              "      <td>-119.375056</td>\n",
              "      <td>1.969668</td>\n",
              "      <td>-123.00</td>\n",
              "      <td>-121.2525</td>\n",
              "      <td>-119.215</td>\n",
              "      <td>-117.5050</td>\n",
              "      <td>-115.59</td>\n",
              "      <td>178.0</td>\n",
              "      <td>35.785899</td>\n",
              "      <td>...</td>\n",
              "      <td>5.324325</td>\n",
              "      <td>14.9009</td>\n",
              "      <td>178.0</td>\n",
              "      <td>190805.073034</td>\n",
              "      <td>98183.414798</td>\n",
              "      <td>22500.0</td>\n",
              "      <td>120600.0</td>\n",
              "      <td>162500.0</td>\n",
              "      <td>248775.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9.0</th>\n",
              "      <td>172.0</td>\n",
              "      <td>-119.324186</td>\n",
              "      <td>1.932799</td>\n",
              "      <td>-123.49</td>\n",
              "      <td>-121.1350</td>\n",
              "      <td>-119.020</td>\n",
              "      <td>-117.5625</td>\n",
              "      <td>-116.22</td>\n",
              "      <td>172.0</td>\n",
              "      <td>35.654942</td>\n",
              "      <td>...</td>\n",
              "      <td>5.437150</td>\n",
              "      <td>10.8289</td>\n",
              "      <td>172.0</td>\n",
              "      <td>190306.994186</td>\n",
              "      <td>97492.006264</td>\n",
              "      <td>54200.0</td>\n",
              "      <td>124775.0</td>\n",
              "      <td>162600.0</td>\n",
              "      <td>227825.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10.0</th>\n",
              "      <td>226.0</td>\n",
              "      <td>-119.350841</td>\n",
              "      <td>1.966870</td>\n",
              "      <td>-123.40</td>\n",
              "      <td>-121.2575</td>\n",
              "      <td>-119.045</td>\n",
              "      <td>-117.6000</td>\n",
              "      <td>-115.52</td>\n",
              "      <td>226.0</td>\n",
              "      <td>35.881681</td>\n",
              "      <td>...</td>\n",
              "      <td>5.181925</td>\n",
              "      <td>12.5420</td>\n",
              "      <td>226.0</td>\n",
              "      <td>178416.393805</td>\n",
              "      <td>96205.687641</td>\n",
              "      <td>53300.0</td>\n",
              "      <td>112625.0</td>\n",
              "      <td>152100.0</td>\n",
              "      <td>220100.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11.0</th>\n",
              "      <td>208.0</td>\n",
              "      <td>-119.517837</td>\n",
              "      <td>2.008043</td>\n",
              "      <td>-124.23</td>\n",
              "      <td>-121.3125</td>\n",
              "      <td>-119.665</td>\n",
              "      <td>-117.6325</td>\n",
              "      <td>-115.51</td>\n",
              "      <td>208.0</td>\n",
              "      <td>35.930288</td>\n",
              "      <td>...</td>\n",
              "      <td>4.657175</td>\n",
              "      <td>13.1477</td>\n",
              "      <td>208.0</td>\n",
              "      <td>182480.312500</td>\n",
              "      <td>97931.083043</td>\n",
              "      <td>50400.0</td>\n",
              "      <td>114450.0</td>\n",
              "      <td>160250.0</td>\n",
              "      <td>225675.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12.0</th>\n",
              "      <td>192.0</td>\n",
              "      <td>-119.405000</td>\n",
              "      <td>2.029160</td>\n",
              "      <td>-124.09</td>\n",
              "      <td>-121.3400</td>\n",
              "      <td>-119.150</td>\n",
              "      <td>-117.4950</td>\n",
              "      <td>-115.53</td>\n",
              "      <td>192.0</td>\n",
              "      <td>35.806458</td>\n",
              "      <td>...</td>\n",
              "      <td>4.963500</td>\n",
              "      <td>10.0760</td>\n",
              "      <td>192.0</td>\n",
              "      <td>181590.640625</td>\n",
              "      <td>94345.428409</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>112575.0</td>\n",
              "      <td>156700.0</td>\n",
              "      <td>228075.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13.0</th>\n",
              "      <td>249.0</td>\n",
              "      <td>-119.406586</td>\n",
              "      <td>1.959151</td>\n",
              "      <td>-124.17</td>\n",
              "      <td>-121.2400</td>\n",
              "      <td>-119.120</td>\n",
              "      <td>-117.6900</td>\n",
              "      <td>-115.50</td>\n",
              "      <td>249.0</td>\n",
              "      <td>35.814819</td>\n",
              "      <td>...</td>\n",
              "      <td>5.038900</td>\n",
              "      <td>9.8346</td>\n",
              "      <td>249.0</td>\n",
              "      <td>188065.473896</td>\n",
              "      <td>94188.280352</td>\n",
              "      <td>43000.0</td>\n",
              "      <td>116100.0</td>\n",
              "      <td>162500.0</td>\n",
              "      <td>237900.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14.0</th>\n",
              "      <td>347.0</td>\n",
              "      <td>-119.510605</td>\n",
              "      <td>2.045823</td>\n",
              "      <td>-124.05</td>\n",
              "      <td>-121.3350</td>\n",
              "      <td>-119.560</td>\n",
              "      <td>-117.6250</td>\n",
              "      <td>-114.57</td>\n",
              "      <td>347.0</td>\n",
              "      <td>35.905159</td>\n",
              "      <td>...</td>\n",
              "      <td>5.041800</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>347.0</td>\n",
              "      <td>191181.288184</td>\n",
              "      <td>96986.243165</td>\n",
              "      <td>32900.0</td>\n",
              "      <td>121050.0</td>\n",
              "      <td>170100.0</td>\n",
              "      <td>240350.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15.0</th>\n",
              "      <td>416.0</td>\n",
              "      <td>-119.471490</td>\n",
              "      <td>2.126938</td>\n",
              "      <td>-124.19</td>\n",
              "      <td>-121.5050</td>\n",
              "      <td>-119.280</td>\n",
              "      <td>-117.6475</td>\n",
              "      <td>-114.31</td>\n",
              "      <td>416.0</td>\n",
              "      <td>35.878341</td>\n",
              "      <td>...</td>\n",
              "      <td>5.089475</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>416.0</td>\n",
              "      <td>181031.024038</td>\n",
              "      <td>94908.428125</td>\n",
              "      <td>32500.0</td>\n",
              "      <td>111550.0</td>\n",
              "      <td>161300.0</td>\n",
              "      <td>228300.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16.0</th>\n",
              "      <td>635.0</td>\n",
              "      <td>-119.530094</td>\n",
              "      <td>2.094814</td>\n",
              "      <td>-124.00</td>\n",
              "      <td>-121.7550</td>\n",
              "      <td>-119.080</td>\n",
              "      <td>-117.6650</td>\n",
              "      <td>-114.60</td>\n",
              "      <td>635.0</td>\n",
              "      <td>35.685748</td>\n",
              "      <td>...</td>\n",
              "      <td>5.446950</td>\n",
              "      <td>12.0088</td>\n",
              "      <td>635.0</td>\n",
              "      <td>200354.507087</td>\n",
              "      <td>98994.507672</td>\n",
              "      <td>14999.0</td>\n",
              "      <td>128300.0</td>\n",
              "      <td>179500.0</td>\n",
              "      <td>261250.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17.0</th>\n",
              "      <td>576.0</td>\n",
              "      <td>-119.597656</td>\n",
              "      <td>2.143916</td>\n",
              "      <td>-124.30</td>\n",
              "      <td>-121.7250</td>\n",
              "      <td>-119.315</td>\n",
              "      <td>-117.7275</td>\n",
              "      <td>-114.56</td>\n",
              "      <td>576.0</td>\n",
              "      <td>35.907882</td>\n",
              "      <td>...</td>\n",
              "      <td>5.025550</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>576.0</td>\n",
              "      <td>191772.244792</td>\n",
              "      <td>101836.425101</td>\n",
              "      <td>27500.0</td>\n",
              "      <td>117375.0</td>\n",
              "      <td>166450.0</td>\n",
              "      <td>242175.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18.0</th>\n",
              "      <td>478.0</td>\n",
              "      <td>-119.640209</td>\n",
              "      <td>2.076978</td>\n",
              "      <td>-124.10</td>\n",
              "      <td>-121.7200</td>\n",
              "      <td>-119.305</td>\n",
              "      <td>-117.8825</td>\n",
              "      <td>-115.22</td>\n",
              "      <td>478.0</td>\n",
              "      <td>35.938054</td>\n",
              "      <td>...</td>\n",
              "      <td>5.093075</td>\n",
              "      <td>11.6017</td>\n",
              "      <td>478.0</td>\n",
              "      <td>192074.715481</td>\n",
              "      <td>107590.164086</td>\n",
              "      <td>47500.0</td>\n",
              "      <td>109175.0</td>\n",
              "      <td>166200.0</td>\n",
              "      <td>255000.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19.0</th>\n",
              "      <td>412.0</td>\n",
              "      <td>-119.541820</td>\n",
              "      <td>2.042815</td>\n",
              "      <td>-124.30</td>\n",
              "      <td>-121.5800</td>\n",
              "      <td>-119.190</td>\n",
              "      <td>-117.8600</td>\n",
              "      <td>-114.47</td>\n",
              "      <td>412.0</td>\n",
              "      <td>35.842597</td>\n",
              "      <td>...</td>\n",
              "      <td>4.782175</td>\n",
              "      <td>15.0000</td>\n",
              "      <td>412.0</td>\n",
              "      <td>196017.752427</td>\n",
              "      <td>110465.017772</td>\n",
              "      <td>14999.0</td>\n",
              "      <td>111900.0</td>\n",
              "      <td>173450.0</td>\n",
              "      <td>255250.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20.0</th>\n",
              "      <td>383.0</td>\n",
              "      <td>-119.862350</td>\n",
              "      <td>2.132411</td>\n",
              "      <td>-124.21</td>\n",
              "      <td>-121.8900</td>\n",
              "      <td>-119.760</td>\n",
              "      <td>-118.0150</td>\n",
              "      <td>-114.57</td>\n",
              "      <td>383.0</td>\n",
              "      <td>36.136449</td>\n",
              "      <td>...</td>\n",
              "      <td>4.632800</td>\n",
              "      <td>12.2092</td>\n",
              "      <td>383.0</td>\n",
              "      <td>192681.751958</td>\n",
              "      <td>111869.959953</td>\n",
              "      <td>32500.0</td>\n",
              "      <td>105100.0</td>\n",
              "      <td>164600.0</td>\n",
              "      <td>247550.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21.0</th>\n",
              "      <td>373.0</td>\n",
              "      <td>-119.560134</td>\n",
              "      <td>2.023540</td>\n",
              "      <td>-124.19</td>\n",
              "      <td>-121.6000</td>\n",
              "      <td>-119.080</td>\n",
              "      <td>-117.8900</td>\n",
              "      <td>-114.60</td>\n",
              "      <td>373.0</td>\n",
              "      <td>35.751635</td>\n",
              "      <td>...</td>\n",
              "      <td>4.650000</td>\n",
              "      <td>11.6833</td>\n",
              "      <td>373.0</td>\n",
              "      <td>200918.536193</td>\n",
              "      <td>115759.422811</td>\n",
              "      <td>25000.0</td>\n",
              "      <td>111500.0</td>\n",
              "      <td>174100.0</td>\n",
              "      <td>268100.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22.0</th>\n",
              "      <td>323.0</td>\n",
              "      <td>-119.598266</td>\n",
              "      <td>1.974017</td>\n",
              "      <td>-124.14</td>\n",
              "      <td>-121.6350</td>\n",
              "      <td>-119.180</td>\n",
              "      <td>-117.9650</td>\n",
              "      <td>-115.55</td>\n",
              "      <td>323.0</td>\n",
              "      <td>35.772879</td>\n",
              "      <td>...</td>\n",
              "      <td>4.734400</td>\n",
              "      <td>11.2463</td>\n",
              "      <td>323.0</td>\n",
              "      <td>213129.173375</td>\n",
              "      <td>121153.981159</td>\n",
              "      <td>34400.0</td>\n",
              "      <td>120800.0</td>\n",
              "      <td>186900.0</td>\n",
              "      <td>276350.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23.0</th>\n",
              "      <td>382.0</td>\n",
              "      <td>-119.590890</td>\n",
              "      <td>2.076365</td>\n",
              "      <td>-124.16</td>\n",
              "      <td>-121.7600</td>\n",
              "      <td>-118.945</td>\n",
              "      <td>-117.8825</td>\n",
              "      <td>-115.37</td>\n",
              "      <td>382.0</td>\n",
              "      <td>35.718115</td>\n",
              "      <td>...</td>\n",
              "      <td>5.077525</td>\n",
              "      <td>12.9792</td>\n",
              "      <td>382.0</td>\n",
              "      <td>204621.787958</td>\n",
              "      <td>115965.729813</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>112800.0</td>\n",
              "      <td>184550.0</td>\n",
              "      <td>259250.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24.0</th>\n",
              "      <td>389.0</td>\n",
              "      <td>-119.344910</td>\n",
              "      <td>1.925299</td>\n",
              "      <td>-124.15</td>\n",
              "      <td>-121.4100</td>\n",
              "      <td>-118.460</td>\n",
              "      <td>-117.9000</td>\n",
              "      <td>-114.73</td>\n",
              "      <td>389.0</td>\n",
              "      <td>35.391054</td>\n",
              "      <td>...</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>14.5833</td>\n",
              "      <td>389.0</td>\n",
              "      <td>207293.365039</td>\n",
              "      <td>111247.078793</td>\n",
              "      <td>32500.0</td>\n",
              "      <td>118800.0</td>\n",
              "      <td>189500.0</td>\n",
              "      <td>265200.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25.0</th>\n",
              "      <td>461.0</td>\n",
              "      <td>-119.426898</td>\n",
              "      <td>1.937591</td>\n",
              "      <td>-124.14</td>\n",
              "      <td>-121.5100</td>\n",
              "      <td>-118.460</td>\n",
              "      <td>-117.9600</td>\n",
              "      <td>-114.58</td>\n",
              "      <td>461.0</td>\n",
              "      <td>35.470108</td>\n",
              "      <td>...</td>\n",
              "      <td>5.285200</td>\n",
              "      <td>13.3913</td>\n",
              "      <td>461.0</td>\n",
              "      <td>223097.223427</td>\n",
              "      <td>118750.787480</td>\n",
              "      <td>45500.0</td>\n",
              "      <td>132700.0</td>\n",
              "      <td>209900.0</td>\n",
              "      <td>278100.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26.0</th>\n",
              "      <td>503.0</td>\n",
              "      <td>-119.477654</td>\n",
              "      <td>1.971470</td>\n",
              "      <td>-124.17</td>\n",
              "      <td>-121.7650</td>\n",
              "      <td>-118.500</td>\n",
              "      <td>-117.9150</td>\n",
              "      <td>-116.00</td>\n",
              "      <td>503.0</td>\n",
              "      <td>35.507038</td>\n",
              "      <td>...</td>\n",
              "      <td>5.267450</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>503.0</td>\n",
              "      <td>206773.793241</td>\n",
              "      <td>112092.926462</td>\n",
              "      <td>36600.0</td>\n",
              "      <td>122150.0</td>\n",
              "      <td>188000.0</td>\n",
              "      <td>260800.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27.0</th>\n",
              "      <td>397.0</td>\n",
              "      <td>-119.708363</td>\n",
              "      <td>1.957695</td>\n",
              "      <td>-124.14</td>\n",
              "      <td>-121.7600</td>\n",
              "      <td>-119.050</td>\n",
              "      <td>-118.0300</td>\n",
              "      <td>-115.54</td>\n",
              "      <td>397.0</td>\n",
              "      <td>35.707128</td>\n",
              "      <td>...</td>\n",
              "      <td>4.711000</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>397.0</td>\n",
              "      <td>206728.259446</td>\n",
              "      <td>114236.712734</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>118800.0</td>\n",
              "      <td>184200.0</td>\n",
              "      <td>267700.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28.0</th>\n",
              "      <td>400.0</td>\n",
              "      <td>-119.568050</td>\n",
              "      <td>2.004950</td>\n",
              "      <td>-124.22</td>\n",
              "      <td>-121.8400</td>\n",
              "      <td>-118.490</td>\n",
              "      <td>-117.9800</td>\n",
              "      <td>-114.65</td>\n",
              "      <td>400.0</td>\n",
              "      <td>35.571075</td>\n",
              "      <td>...</td>\n",
              "      <td>4.644000</td>\n",
              "      <td>14.4113</td>\n",
              "      <td>400.0</td>\n",
              "      <td>210049.812500</td>\n",
              "      <td>119811.441221</td>\n",
              "      <td>40900.0</td>\n",
              "      <td>119600.0</td>\n",
              "      <td>187450.0</td>\n",
              "      <td>269075.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29.0</th>\n",
              "      <td>374.0</td>\n",
              "      <td>-119.360348</td>\n",
              "      <td>1.954722</td>\n",
              "      <td>-124.14</td>\n",
              "      <td>-121.4475</td>\n",
              "      <td>-118.440</td>\n",
              "      <td>-117.9600</td>\n",
              "      <td>-114.58</td>\n",
              "      <td>374.0</td>\n",
              "      <td>35.427968</td>\n",
              "      <td>...</td>\n",
              "      <td>4.375000</td>\n",
              "      <td>13.9470</td>\n",
              "      <td>374.0</td>\n",
              "      <td>195185.863636</td>\n",
              "      <td>117739.536913</td>\n",
              "      <td>28300.0</td>\n",
              "      <td>102275.0</td>\n",
              "      <td>169550.0</td>\n",
              "      <td>248700.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30.0</th>\n",
              "      <td>384.0</td>\n",
              "      <td>-119.499922</td>\n",
              "      <td>1.933400</td>\n",
              "      <td>-124.19</td>\n",
              "      <td>-121.6700</td>\n",
              "      <td>-118.430</td>\n",
              "      <td>-118.0400</td>\n",
              "      <td>-115.37</td>\n",
              "      <td>384.0</td>\n",
              "      <td>35.476120</td>\n",
              "      <td>...</td>\n",
              "      <td>4.462775</td>\n",
              "      <td>12.4429</td>\n",
              "      <td>384.0</td>\n",
              "      <td>201559.145833</td>\n",
              "      <td>112936.213780</td>\n",
              "      <td>42500.0</td>\n",
              "      <td>120475.0</td>\n",
              "      <td>171550.0</td>\n",
              "      <td>254050.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31.0</th>\n",
              "      <td>384.0</td>\n",
              "      <td>-119.412865</td>\n",
              "      <td>1.962201</td>\n",
              "      <td>-124.13</td>\n",
              "      <td>-121.4400</td>\n",
              "      <td>-118.420</td>\n",
              "      <td>-118.0175</td>\n",
              "      <td>-114.61</td>\n",
              "      <td>384.0</td>\n",
              "      <td>35.370990</td>\n",
              "      <td>...</td>\n",
              "      <td>4.309400</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>384.0</td>\n",
              "      <td>208319.851562</td>\n",
              "      <td>120693.635055</td>\n",
              "      <td>34200.0</td>\n",
              "      <td>120350.0</td>\n",
              "      <td>183850.0</td>\n",
              "      <td>267225.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32.0</th>\n",
              "      <td>458.0</td>\n",
              "      <td>-119.465917</td>\n",
              "      <td>1.933148</td>\n",
              "      <td>-124.25</td>\n",
              "      <td>-121.4975</td>\n",
              "      <td>-118.425</td>\n",
              "      <td>-118.0225</td>\n",
              "      <td>-115.37</td>\n",
              "      <td>458.0</td>\n",
              "      <td>35.472664</td>\n",
              "      <td>...</td>\n",
              "      <td>4.464600</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>458.0</td>\n",
              "      <td>207131.500000</td>\n",
              "      <td>121348.298305</td>\n",
              "      <td>43100.0</td>\n",
              "      <td>113725.0</td>\n",
              "      <td>181600.0</td>\n",
              "      <td>260850.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33.0</th>\n",
              "      <td>513.0</td>\n",
              "      <td>-119.394211</td>\n",
              "      <td>1.953711</td>\n",
              "      <td>-124.18</td>\n",
              "      <td>-121.5100</td>\n",
              "      <td>-118.390</td>\n",
              "      <td>-117.9500</td>\n",
              "      <td>-115.46</td>\n",
              "      <td>513.0</td>\n",
              "      <td>35.402378</td>\n",
              "      <td>...</td>\n",
              "      <td>4.660700</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>513.0</td>\n",
              "      <td>203922.269006</td>\n",
              "      <td>114256.560418</td>\n",
              "      <td>22500.0</td>\n",
              "      <td>125000.0</td>\n",
              "      <td>175000.0</td>\n",
              "      <td>255600.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34.0</th>\n",
              "      <td>567.0</td>\n",
              "      <td>-119.195714</td>\n",
              "      <td>1.838514</td>\n",
              "      <td>-124.18</td>\n",
              "      <td>-121.2750</td>\n",
              "      <td>-118.350</td>\n",
              "      <td>-118.0050</td>\n",
              "      <td>-114.59</td>\n",
              "      <td>567.0</td>\n",
              "      <td>35.174603</td>\n",
              "      <td>...</td>\n",
              "      <td>4.756650</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>567.0</td>\n",
              "      <td>213061.615520</td>\n",
              "      <td>122656.145392</td>\n",
              "      <td>26600.0</td>\n",
              "      <td>125250.0</td>\n",
              "      <td>185800.0</td>\n",
              "      <td>258050.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35.0</th>\n",
              "      <td>692.0</td>\n",
              "      <td>-119.233786</td>\n",
              "      <td>1.823856</td>\n",
              "      <td>-124.18</td>\n",
              "      <td>-121.2625</td>\n",
              "      <td>-118.350</td>\n",
              "      <td>-118.0100</td>\n",
              "      <td>-115.38</td>\n",
              "      <td>692.0</td>\n",
              "      <td>35.163627</td>\n",
              "      <td>...</td>\n",
              "      <td>4.838775</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>692.0</td>\n",
              "      <td>205624.462428</td>\n",
              "      <td>108101.766134</td>\n",
              "      <td>39600.0</td>\n",
              "      <td>133850.0</td>\n",
              "      <td>184250.0</td>\n",
              "      <td>254750.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36.0</th>\n",
              "      <td>715.0</td>\n",
              "      <td>-119.232434</td>\n",
              "      <td>1.775785</td>\n",
              "      <td>-124.27</td>\n",
              "      <td>-121.3050</td>\n",
              "      <td>-118.310</td>\n",
              "      <td>-118.1000</td>\n",
              "      <td>-116.56</td>\n",
              "      <td>715.0</td>\n",
              "      <td>35.127972</td>\n",
              "      <td>...</td>\n",
              "      <td>4.775250</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>715.0</td>\n",
              "      <td>208262.422378</td>\n",
              "      <td>106735.813804</td>\n",
              "      <td>14999.0</td>\n",
              "      <td>142050.0</td>\n",
              "      <td>189700.0</td>\n",
              "      <td>248900.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37.0</th>\n",
              "      <td>437.0</td>\n",
              "      <td>-119.464371</td>\n",
              "      <td>1.949705</td>\n",
              "      <td>-124.19</td>\n",
              "      <td>-121.7700</td>\n",
              "      <td>-118.340</td>\n",
              "      <td>-118.1100</td>\n",
              "      <td>-115.60</td>\n",
              "      <td>437.0</td>\n",
              "      <td>35.335378</td>\n",
              "      <td>...</td>\n",
              "      <td>4.419600</td>\n",
              "      <td>13.3670</td>\n",
              "      <td>437.0</td>\n",
              "      <td>209008.299771</td>\n",
              "      <td>119736.766750</td>\n",
              "      <td>42100.0</td>\n",
              "      <td>118300.0</td>\n",
              "      <td>175900.0</td>\n",
              "      <td>267500.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38.0</th>\n",
              "      <td>318.0</td>\n",
              "      <td>-119.334245</td>\n",
              "      <td>1.918090</td>\n",
              "      <td>-124.14</td>\n",
              "      <td>-121.4200</td>\n",
              "      <td>-118.360</td>\n",
              "      <td>-118.1200</td>\n",
              "      <td>-115.38</td>\n",
              "      <td>318.0</td>\n",
              "      <td>35.325472</td>\n",
              "      <td>...</td>\n",
              "      <td>4.128450</td>\n",
              "      <td>12.7823</td>\n",
              "      <td>318.0</td>\n",
              "      <td>195272.371069</td>\n",
              "      <td>120893.707372</td>\n",
              "      <td>39400.0</td>\n",
              "      <td>106375.0</td>\n",
              "      <td>161600.0</td>\n",
              "      <td>248175.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39.0</th>\n",
              "      <td>302.0</td>\n",
              "      <td>-119.621556</td>\n",
              "      <td>1.908547</td>\n",
              "      <td>-124.18</td>\n",
              "      <td>-121.9175</td>\n",
              "      <td>-118.395</td>\n",
              "      <td>-118.1900</td>\n",
              "      <td>-117.07</td>\n",
              "      <td>302.0</td>\n",
              "      <td>35.513146</td>\n",
              "      <td>...</td>\n",
              "      <td>4.121875</td>\n",
              "      <td>11.8603</td>\n",
              "      <td>302.0</td>\n",
              "      <td>206718.913907</td>\n",
              "      <td>125389.522805</td>\n",
              "      <td>17500.0</td>\n",
              "      <td>108750.0</td>\n",
              "      <td>171500.0</td>\n",
              "      <td>273250.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40.0</th>\n",
              "      <td>249.0</td>\n",
              "      <td>-119.576386</td>\n",
              "      <td>1.851377</td>\n",
              "      <td>-122.82</td>\n",
              "      <td>-121.6200</td>\n",
              "      <td>-118.370</td>\n",
              "      <td>-118.1900</td>\n",
              "      <td>-116.98</td>\n",
              "      <td>249.0</td>\n",
              "      <td>35.466265</td>\n",
              "      <td>...</td>\n",
              "      <td>4.044600</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>249.0</td>\n",
              "      <td>205783.594378</td>\n",
              "      <td>123428.327920</td>\n",
              "      <td>43000.0</td>\n",
              "      <td>112500.0</td>\n",
              "      <td>172200.0</td>\n",
              "      <td>247300.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41.0</th>\n",
              "      <td>232.0</td>\n",
              "      <td>-119.390086</td>\n",
              "      <td>1.785995</td>\n",
              "      <td>-123.98</td>\n",
              "      <td>-121.3825</td>\n",
              "      <td>-118.390</td>\n",
              "      <td>-118.2000</td>\n",
              "      <td>-114.59</td>\n",
              "      <td>232.0</td>\n",
              "      <td>35.316983</td>\n",
              "      <td>...</td>\n",
              "      <td>3.911450</td>\n",
              "      <td>8.8619</td>\n",
              "      <td>232.0</td>\n",
              "      <td>197328.935345</td>\n",
              "      <td>125630.370118</td>\n",
              "      <td>41800.0</td>\n",
              "      <td>100675.0</td>\n",
              "      <td>160800.0</td>\n",
              "      <td>247625.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42.0</th>\n",
              "      <td>308.0</td>\n",
              "      <td>-119.480292</td>\n",
              "      <td>1.827079</td>\n",
              "      <td>-124.10</td>\n",
              "      <td>-121.4900</td>\n",
              "      <td>-118.355</td>\n",
              "      <td>-118.1900</td>\n",
              "      <td>-117.07</td>\n",
              "      <td>308.0</td>\n",
              "      <td>35.361169</td>\n",
              "      <td>...</td>\n",
              "      <td>4.013025</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>308.0</td>\n",
              "      <td>199635.110390</td>\n",
              "      <td>112547.856076</td>\n",
              "      <td>39400.0</td>\n",
              "      <td>120000.0</td>\n",
              "      <td>168350.0</td>\n",
              "      <td>242600.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43.0</th>\n",
              "      <td>286.0</td>\n",
              "      <td>-119.689301</td>\n",
              "      <td>1.913316</td>\n",
              "      <td>-124.17</td>\n",
              "      <td>-121.8675</td>\n",
              "      <td>-118.410</td>\n",
              "      <td>-118.2100</td>\n",
              "      <td>-117.01</td>\n",
              "      <td>286.0</td>\n",
              "      <td>35.582587</td>\n",
              "      <td>...</td>\n",
              "      <td>4.019875</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>286.0</td>\n",
              "      <td>194365.779720</td>\n",
              "      <td>116726.685274</td>\n",
              "      <td>46600.0</td>\n",
              "      <td>105925.0</td>\n",
              "      <td>162500.0</td>\n",
              "      <td>253150.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44.0</th>\n",
              "      <td>296.0</td>\n",
              "      <td>-119.460034</td>\n",
              "      <td>1.865856</td>\n",
              "      <td>-124.09</td>\n",
              "      <td>-121.6500</td>\n",
              "      <td>-118.340</td>\n",
              "      <td>-118.1800</td>\n",
              "      <td>-115.73</td>\n",
              "      <td>296.0</td>\n",
              "      <td>35.276351</td>\n",
              "      <td>...</td>\n",
              "      <td>4.314225</td>\n",
              "      <td>12.4237</td>\n",
              "      <td>296.0</td>\n",
              "      <td>214909.506757</td>\n",
              "      <td>124080.375756</td>\n",
              "      <td>48300.0</td>\n",
              "      <td>112500.0</td>\n",
              "      <td>178750.0</td>\n",
              "      <td>295225.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45.0</th>\n",
              "      <td>235.0</td>\n",
              "      <td>-119.668596</td>\n",
              "      <td>1.913955</td>\n",
              "      <td>-123.22</td>\n",
              "      <td>-122.1450</td>\n",
              "      <td>-118.380</td>\n",
              "      <td>-118.1850</td>\n",
              "      <td>-117.12</td>\n",
              "      <td>235.0</td>\n",
              "      <td>35.468979</td>\n",
              "      <td>...</td>\n",
              "      <td>4.546200</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>235.0</td>\n",
              "      <td>223464.744681</td>\n",
              "      <td>116254.957305</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>140050.0</td>\n",
              "      <td>198400.0</td>\n",
              "      <td>280150.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46.0</th>\n",
              "      <td>196.0</td>\n",
              "      <td>-119.877755</td>\n",
              "      <td>2.030780</td>\n",
              "      <td>-124.16</td>\n",
              "      <td>-122.1625</td>\n",
              "      <td>-118.470</td>\n",
              "      <td>-118.2600</td>\n",
              "      <td>-114.60</td>\n",
              "      <td>196.0</td>\n",
              "      <td>35.732194</td>\n",
              "      <td>...</td>\n",
              "      <td>3.981700</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>196.0</td>\n",
              "      <td>198036.260204</td>\n",
              "      <td>109881.865243</td>\n",
              "      <td>26900.0</td>\n",
              "      <td>110550.0</td>\n",
              "      <td>174250.0</td>\n",
              "      <td>246925.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47.0</th>\n",
              "      <td>175.0</td>\n",
              "      <td>-119.985029</td>\n",
              "      <td>1.969042</td>\n",
              "      <td>-124.15</td>\n",
              "      <td>-122.1700</td>\n",
              "      <td>-118.480</td>\n",
              "      <td>-118.2700</td>\n",
              "      <td>-117.10</td>\n",
              "      <td>175.0</td>\n",
              "      <td>35.843943</td>\n",
              "      <td>...</td>\n",
              "      <td>3.995200</td>\n",
              "      <td>9.4590</td>\n",
              "      <td>175.0</td>\n",
              "      <td>190064.611429</td>\n",
              "      <td>110788.506693</td>\n",
              "      <td>49600.0</td>\n",
              "      <td>108850.0</td>\n",
              "      <td>162900.0</td>\n",
              "      <td>235800.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48.0</th>\n",
              "      <td>135.0</td>\n",
              "      <td>-119.837556</td>\n",
              "      <td>1.993466</td>\n",
              "      <td>-122.97</td>\n",
              "      <td>-122.1650</td>\n",
              "      <td>-118.450</td>\n",
              "      <td>-118.2850</td>\n",
              "      <td>-114.61</td>\n",
              "      <td>135.0</td>\n",
              "      <td>35.730222</td>\n",
              "      <td>...</td>\n",
              "      <td>4.002500</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>135.0</td>\n",
              "      <td>224665.251852</td>\n",
              "      <td>138057.219438</td>\n",
              "      <td>48600.0</td>\n",
              "      <td>104350.0</td>\n",
              "      <td>189700.0</td>\n",
              "      <td>324650.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49.0</th>\n",
              "      <td>111.0</td>\n",
              "      <td>-120.374324</td>\n",
              "      <td>2.047430</td>\n",
              "      <td>-123.79</td>\n",
              "      <td>-122.2800</td>\n",
              "      <td>-121.230</td>\n",
              "      <td>-118.2900</td>\n",
              "      <td>-117.10</td>\n",
              "      <td>111.0</td>\n",
              "      <td>36.097748</td>\n",
              "      <td>...</td>\n",
              "      <td>4.262500</td>\n",
              "      <td>12.9591</td>\n",
              "      <td>111.0</td>\n",
              "      <td>220667.612613</td>\n",
              "      <td>115402.562465</td>\n",
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              "      <td>283350.0</td>\n",
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              "      <th>50.0</th>\n",
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              "      <td>-120.077143</td>\n",
              "      <td>2.054972</td>\n",
              "      <td>-124.16</td>\n",
              "      <td>-122.2225</td>\n",
              "      <td>-119.505</td>\n",
              "      <td>-118.2700</td>\n",
              "      <td>-117.11</td>\n",
              "      <td>112.0</td>\n",
              "      <td>35.900893</td>\n",
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              "      <td>3.847775</td>\n",
              "      <td>13.3036</td>\n",
              "      <td>112.0</td>\n",
              "      <td>222513.482143</td>\n",
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              "      <td>54200.0</td>\n",
              "      <td>125000.0</td>\n",
              "      <td>176650.0</td>\n",
              "      <td>316550.0</td>\n",
              "      <td>500001.0</td>\n",
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              "    <tr>\n",
              "      <th>51.0</th>\n",
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              "      <td>-120.593750</td>\n",
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              "      <td>-122.48</td>\n",
              "      <td>-122.2850</td>\n",
              "      <td>-122.075</td>\n",
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              "      <td>36.325937</td>\n",
              "      <td>...</td>\n",
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              "      <td>322275.0</td>\n",
              "      <td>500001.0</td>\n",
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              "    <tr>\n",
              "      <th>52.0</th>\n",
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              "      <td>-120.917253</td>\n",
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              "      <td>-118.3500</td>\n",
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              "<p>52 rows × 64 columns</p>\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.groupby(\"housing_median_age\").get_group(15.0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "i0_HSa6bLk9N",
        "outputId": "f91962ea-7f30-4a25-ab87-9bcce8191425"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0        -114.31     34.19                15.0       5612.0          1283.0   \n",
              "14       -114.63     32.76                15.0       1448.0           378.0   \n",
              "23       -114.98     33.82                15.0        644.0           129.0   \n",
              "38       -115.48     32.68                15.0       3414.0           666.0   \n",
              "79       -115.56     32.80                15.0       1171.0           328.0   \n",
              "...          ...       ...                 ...          ...             ...   \n",
              "16836    -123.34     39.50                15.0       2342.0           535.0   \n",
              "16852    -123.48     40.79                15.0        619.0           160.0   \n",
              "16858    -123.58     39.66                15.0       1839.0           489.0   \n",
              "16957    -124.16     41.74                15.0       2715.0           569.0   \n",
              "16983    -124.19     41.78                15.0       3140.0           714.0   \n",
              "\n",
              "       population  households  median_income  median_house_value  \n",
              "0          1015.0       472.0         1.4936             66900.0  \n",
              "14          949.0       300.0         0.8585             45000.0  \n",
              "23          137.0        52.0         3.2097             71300.0  \n",
              "38         2097.0       622.0         2.3319             91200.0  \n",
              "79         1024.0       298.0         1.3882             69400.0  \n",
              "...           ...         ...            ...                 ...  \n",
              "16836      1064.0       433.0         1.8967             96600.0  \n",
              "16852       287.0       104.0         1.9107             79200.0  \n",
              "16858       887.0       332.0         2.2429            100000.0  \n",
              "16957      1532.0       530.0         2.1829             69500.0  \n",
              "16983      1645.0       640.0         1.6654             74600.0  \n",
              "\n",
              "[416 rows x 9 columns]"
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              "      <th></th>\n",
              "      <th>longitude</th>\n",
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              "      <th>housing_median_age</th>\n",
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              "      <td>666.0</td>\n",
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              "<p>416 rows × 9 columns</p>\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#get all data groups with indices of rows\n",
        "#housing_data.groupby(\"housing_median_age\").indices\n",
        "\n",
        "#get a Series object for a certain group\n",
        "#housing_data.groupby(\"housing_median_age\").longitude.get_group(15.0)"
      ],
      "metadata": {
        "id": "p_rlXDKAQU7E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "One function we've been using heavily thus far is the value_counts() function. We can replicate what value_counts() does by doing the following:\n",
        "\n"
      ],
      "metadata": {
        "id": "KTdlta9cRMb1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.groupby('housing_median_age').housing_median_age.count()\n",
        "\n",
        "#other functions of describe() are also available\n",
        "#This is a groupBy object NOT standard series or dataframe. Hence doesnot have 'loc'.\n",
        "#To get a specifically we need get_groups which returns DataFrame object or Series object"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qP0E5l3HRNQ5",
        "outputId": "c64a7ce0-cfa4-414e-b30a-0a4db71f7cc7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "housing_median_age\n",
              "1.0        2\n",
              "2.0       49\n",
              "3.0       46\n",
              "4.0      161\n",
              "5.0      199\n",
              "6.0      129\n",
              "7.0      151\n",
              "8.0      178\n",
              "9.0      172\n",
              "10.0     226\n",
              "11.0     208\n",
              "12.0     192\n",
              "13.0     249\n",
              "14.0     347\n",
              "15.0     416\n",
              "16.0     635\n",
              "17.0     576\n",
              "18.0     478\n",
              "19.0     412\n",
              "20.0     383\n",
              "21.0     373\n",
              "22.0     323\n",
              "23.0     382\n",
              "24.0     389\n",
              "25.0     461\n",
              "26.0     503\n",
              "27.0     397\n",
              "28.0     400\n",
              "29.0     374\n",
              "30.0     384\n",
              "31.0     384\n",
              "32.0     458\n",
              "33.0     513\n",
              "34.0     567\n",
              "35.0     692\n",
              "36.0     715\n",
              "37.0     437\n",
              "38.0     318\n",
              "39.0     302\n",
              "40.0     249\n",
              "41.0     232\n",
              "42.0     308\n",
              "43.0     286\n",
              "44.0     296\n",
              "45.0     235\n",
              "46.0     196\n",
              "47.0     175\n",
              "48.0     135\n",
              "49.0     111\n",
              "50.0     112\n",
              "51.0      32\n",
              "52.0    1052\n",
              "Name: housing_median_age, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.groupby(\"housing_median_age\").apply(lambda df: df.loc[df.latitude >= 35])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "hNljNX5hS6xJ",
        "outputId": "ff97db20-0090-4747-cbf3-e0d891b36160"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "                          longitude  latitude  housing_median_age  \\\n",
              "housing_median_age                                                  \n",
              "1.0                10993    -120.93     37.65                 1.0   \n",
              "                   13708    -122.00     38.23                 1.0   \n",
              "2.0                10207    -119.88     36.83                 2.0   \n",
              "                   10952    -120.88     37.52                 2.0   \n",
              "                   11280    -121.13     38.66                 2.0   \n",
              "...                             ...       ...                 ...   \n",
              "52.0               16971    -124.17     40.80                52.0   \n",
              "                   16992    -124.23     40.81                52.0   \n",
              "                   16993    -124.23     40.54                52.0   \n",
              "                   16995    -124.26     40.58                52.0   \n",
              "                   16999    -124.35     40.54                52.0   \n",
              "\n",
              "                          total_rooms  total_bedrooms  population  households  \\\n",
              "housing_median_age                                                              \n",
              "1.0                10993       2254.0           328.0       402.0       112.0   \n",
              "                   13708       2062.0           343.0       872.0       268.0   \n",
              "2.0                10207       4055.0           735.0      1730.0       654.0   \n",
              "                   10952       1871.0           409.0       707.0       256.0   \n",
              "                   11280      12360.0          1747.0      4438.0      1470.0   \n",
              "...                               ...             ...         ...         ...   \n",
              "52.0               16971       1557.0           344.0       758.0       319.0   \n",
              "                   16992       1112.0           209.0       544.0       172.0   \n",
              "                   16993       2694.0           453.0      1152.0       435.0   \n",
              "                   16995       2217.0           394.0       907.0       369.0   \n",
              "                   16999       1820.0           300.0       806.0       270.0   \n",
              "\n",
              "                          median_income  median_house_value  \n",
              "housing_median_age                                           \n",
              "1.0                10993         4.2500            189200.0  \n",
              "                   13708         5.2636            191300.0  \n",
              "2.0                10207         4.2132             96500.0  \n",
              "                   10952         2.6103            133600.0  \n",
              "                   11280         6.2503            222500.0  \n",
              "...                                 ...                 ...  \n",
              "52.0               16971         1.8529             62500.0  \n",
              "                   16992         3.3462             50800.0  \n",
              "                   16993         3.0806            106700.0  \n",
              "                   16995         2.3571            111400.0  \n",
              "                   16999         3.0147             94600.0  \n",
              "\n",
              "[7662 rows x 9 columns]"
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "idxmax()  ==> returns index of maximum value"
      ],
      "metadata": {
        "id": "8UOUa2tUvwnb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# grouped by median_age, returns a DF with max no of rooms.\n",
        "\n",
        "housing_data.groupby(\"housing_median_age\").apply(lambda df: df.loc[df.total_rooms.idxmax()])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "TSSkB5WPu6TE",
        "outputId": "02256a14-4b4b-4a72-e53b-7212f5c1a268"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    longitude  latitude  housing_median_age  total_rooms  \\\n",
              "housing_median_age                                                         \n",
              "1.0                   -120.93     37.65                 1.0       2254.0   \n",
              "2.0                   -121.35     38.72                 2.0      21897.0   \n",
              "3.0                   -117.19     33.90                 3.0      21060.0   \n",
              "4.0                   -117.74     33.89                 4.0      37937.0   \n",
              "5.0                   -118.90     34.26                 5.0      25187.0   \n",
              "6.0                   -117.58     33.85                 6.0      16431.0   \n",
              "7.0                   -121.92     37.53                 7.0      28258.0   \n",
              "8.0                   -117.78     34.03                 8.0      32054.0   \n",
              "9.0                   -118.78     34.16                 9.0      30405.0   \n",
              "10.0                  -117.59     34.13                10.0      20263.0   \n",
              "11.0                  -121.79     36.64                11.0      32627.0   \n",
              "12.0                  -118.46     34.40                12.0      25957.0   \n",
              "13.0                  -117.81     34.08                13.0      18448.0   \n",
              "14.0                  -117.42     33.35                14.0      25135.0   \n",
              "15.0                  -117.95     33.98                15.0      16042.0   \n",
              "16.0                  -117.88     33.96                16.0      19059.0   \n",
              "17.0                  -117.71     34.15                17.0      17715.0   \n",
              "18.0                  -119.05     35.33                18.0      12707.0   \n",
              "19.0                  -118.88     34.02                19.0      15990.0   \n",
              "20.0                  -118.52     34.17                20.0      17377.0   \n",
              "21.0                  -122.46     37.59                21.0      12902.0   \n",
              "22.0                  -118.42     33.75                22.0      17591.0   \n",
              "23.0                  -122.14     37.36                23.0      11294.0   \n",
              "24.0                  -117.84     33.82                24.0      10281.0   \n",
              "25.0                  -118.34     33.78                25.0      11016.0   \n",
              "26.0                  -116.97     32.78                26.0       8902.0   \n",
              "27.0                  -116.94     34.24                27.0      12342.0   \n",
              "28.0                  -118.06     34.13                28.0      12139.0   \n",
              "29.0                  -120.59     34.70                29.0      17738.0   \n",
              "30.0                  -118.00     33.97                30.0       6540.0   \n",
              "31.0                  -118.48     34.14                31.0       9320.0   \n",
              "32.0                  -118.26     34.18                32.0      14556.0   \n",
              "33.0                  -122.57     38.02                33.0       9531.0   \n",
              "34.0                  -119.23     34.46                34.0       9280.0   \n",
              "35.0                  -121.94     38.27                35.0      10869.0   \n",
              "36.0                  -118.36     34.13                36.0       6871.0   \n",
              "37.0                  -118.53     34.09                37.0       5477.0   \n",
              "38.0                  -122.57     37.99                38.0       5587.0   \n",
              "39.0                  -117.37     34.59                39.0       8193.0   \n",
              "40.0                  -122.61     37.99                40.0       7737.0   \n",
              "41.0                  -122.13     37.74                41.0       4400.0   \n",
              "42.0                  -122.48     37.85                42.0       6297.0   \n",
              "43.0                  -118.41     34.06                43.0       4994.0   \n",
              "44.0                  -122.26     37.50                44.0       6983.0   \n",
              "45.0                  -118.35     34.07                45.0       7803.0   \n",
              "46.0                  -122.45     37.74                46.0       6429.0   \n",
              "47.0                  -122.57     37.97                47.0       5416.0   \n",
              "48.0                  -122.42     37.79                48.0       4506.0   \n",
              "49.0                  -117.10     32.77                49.0       4449.0   \n",
              "50.0                  -118.22     34.14                50.0       3657.0   \n",
              "51.0                  -121.49     38.55                51.0       4280.0   \n",
              "52.0                  -122.41     37.79                52.0       6016.0   \n",
              "\n",
              "                    total_bedrooms  population  households  median_income  \\\n",
              "housing_median_age                                                          \n",
              "1.0                          328.0       402.0       112.0         4.2500   \n",
              "2.0                         3513.0      8652.0      2873.0         4.5432   \n",
              "3.0                         3366.0      9623.0      2812.0         4.1890   \n",
              "4.0                         5471.0     16122.0      5189.0         7.4947   \n",
              "5.0                         3521.0     11956.0      3478.0         6.9712   \n",
              "6.0                         2640.0      8222.0      2553.0         5.2861   \n",
              "7.0                         3864.0     12203.0      3701.0         8.4045   \n",
              "8.0                         5290.0     15507.0      5050.0         6.0191   \n",
              "9.0                         4093.0     12873.0      3931.0         8.0137   \n",
              "10.0                        3915.0      9716.0      3744.0         3.8505   \n",
              "11.0                        6445.0     28566.0      6082.0         2.3087   \n",
              "12.0                        4798.0     10475.0      4490.0         4.5420   \n",
              "13.0                        2474.0      7775.0      2397.0         7.7876   \n",
              "14.0                        4819.0     35682.0      4769.0         2.5729   \n",
              "15.0                        2602.0      7732.0      2552.0         5.6716   \n",
              "16.0                        3079.0     10988.0      3061.0         5.5469   \n",
              "17.0                        2370.0      7665.0      2312.0         7.9068   \n",
              "18.0                        2685.0      7009.0      2552.0         2.9438   \n",
              "19.0                        2611.0      5175.0      2173.0         7.7848   \n",
              "20.0                        4457.0      7450.0      4204.0         3.2154   \n",
              "21.0                        2118.0      6160.0      2082.0         5.7653   \n",
              "22.0                        2604.0      6897.0      2492.0         8.2831   \n",
              "23.0                        1377.0      3840.0      1367.0        12.1387   \n",
              "24.0                        1689.0      4926.0      1629.0         4.7946   \n",
              "25.0                        1626.0      4168.0      1584.0         8.1782   \n",
              "26.0                        1413.0      3941.0      1387.0         4.7943   \n",
              "27.0                        2630.0      1300.0       566.0         1.9980   \n",
              "28.0                        2873.0      5359.0      2731.0         3.2920   \n",
              "29.0                        3114.0     12427.0      2826.0         2.7377   \n",
              "30.0                         991.0      3124.0       953.0         6.0663   \n",
              "31.0                        1143.0      2980.0      1109.0        10.3599   \n",
              "32.0                        2077.0      5459.0      2017.0         8.1657   \n",
              "33.0                        1487.0      3798.0      1409.0         5.6512   \n",
              "34.0                        1765.0      4514.0      1693.0         3.2026   \n",
              "35.0                        2226.0      9879.0      2152.0         2.5681   \n",
              "36.0                        1180.0      2216.0      1130.0         8.0499   \n",
              "37.0                         833.0      1925.0       757.0         8.1888   \n",
              "38.0                         996.0      2466.0      1027.0         4.1711   \n",
              "39.0                        1747.0      6852.0      1597.0         2.3832   \n",
              "40.0                        1488.0      3108.0      1349.0         4.4375   \n",
              "41.0                         666.0      1476.0       648.0         5.0000   \n",
              "42.0                        1307.0      2096.0      1205.0         6.4752   \n",
              "43.0                        1057.0      1830.0       969.0         5.5321   \n",
              "44.0                        1131.0      2818.0      1115.0         5.6271   \n",
              "45.0                        2154.0      3359.0      2041.0         3.3594   \n",
              "46.0                        1093.0      2535.0      1109.0         5.0887   \n",
              "47.0                        1115.0      2177.0      1027.0         3.5055   \n",
              "48.0                        1342.0      1980.0      1239.0         4.0156   \n",
              "49.0                         711.0      1606.0       709.0         5.7768   \n",
              "50.0                         708.0      1725.0       644.0         5.5456   \n",
              "51.0                         632.0      1486.0       621.0         5.0359   \n",
              "52.0                        2509.0      3436.0      2119.0         2.5166   \n",
              "\n",
              "                    median_house_value  \n",
              "housing_median_age                      \n",
              "1.0                           189200.0  \n",
              "2.0                           151300.0  \n",
              "3.0                           143000.0  \n",
              "4.0                           366300.0  \n",
              "5.0                           321300.0  \n",
              "6.0                           195100.0  \n",
              "7.0                           451100.0  \n",
              "8.0                           253900.0  \n",
              "9.0                           399200.0  \n",
              "10.0                          169600.0  \n",
              "11.0                          118800.0  \n",
              "12.0                          195300.0  \n",
              "13.0                          348900.0  \n",
              "14.0                          134400.0  \n",
              "15.0                          330400.0  \n",
              "16.0                          265200.0  \n",
              "17.0                          349100.0  \n",
              "18.0                           87200.0  \n",
              "19.0                          500001.0  \n",
              "20.0                          259600.0  \n",
              "21.0                          325800.0  \n",
              "22.0                          500001.0  \n",
              "23.0                          500001.0  \n",
              "24.0                          251200.0  \n",
              "25.0                          500001.0  \n",
              "26.0                          226900.0  \n",
              "27.0                          153500.0  \n",
              "28.0                          227300.0  \n",
              "29.0                           28300.0  \n",
              "30.0                          372600.0  \n",
              "31.0                          500001.0  \n",
              "32.0                          500001.0  \n",
              "33.0                          314000.0  \n",
              "34.0                          227600.0  \n",
              "35.0                           81300.0  \n",
              "36.0                          495600.0  \n",
              "37.0                          500001.0  \n",
              "38.0                          336900.0  \n",
              "39.0                           35000.0  \n",
              "40.0                          289600.0  \n",
              "41.0                          248900.0  \n",
              "42.0                          500001.0  \n",
              "43.0                          500001.0  \n",
              "44.0                          374800.0  \n",
              "45.0                          287500.0  \n",
              "46.0                          335100.0  \n",
              "47.0                          382100.0  \n",
              "48.0                          500001.0  \n",
              "49.0                          281600.0  \n",
              "50.0                          258100.0  \n",
              "51.0                          224100.0  \n",
              "52.0                          275000.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6d9d9866-bc8e-4e7e-bc70-2e0d7cc170ec\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>housing_median_age</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>1.0</th>\n",
              "      <td>-120.93</td>\n",
              "      <td>37.65</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2254.0</td>\n",
              "      <td>328.0</td>\n",
              "      <td>402.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>4.2500</td>\n",
              "      <td>189200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>-121.35</td>\n",
              "      <td>38.72</td>\n",
              "      <td>2.0</td>\n",
              "      <td>21897.0</td>\n",
              "      <td>3513.0</td>\n",
              "      <td>8652.0</td>\n",
              "      <td>2873.0</td>\n",
              "      <td>4.5432</td>\n",
              "      <td>151300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <td>-117.19</td>\n",
              "      <td>33.90</td>\n",
              "      <td>3.0</td>\n",
              "      <td>21060.0</td>\n",
              "      <td>3366.0</td>\n",
              "      <td>9623.0</td>\n",
              "      <td>2812.0</td>\n",
              "      <td>4.1890</td>\n",
              "      <td>143000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.0</th>\n",
              "      <td>-117.74</td>\n",
              "      <td>33.89</td>\n",
              "      <td>4.0</td>\n",
              "      <td>37937.0</td>\n",
              "      <td>5471.0</td>\n",
              "      <td>16122.0</td>\n",
              "      <td>5189.0</td>\n",
              "      <td>7.4947</td>\n",
              "      <td>366300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.0</th>\n",
              "      <td>-118.90</td>\n",
              "      <td>34.26</td>\n",
              "      <td>5.0</td>\n",
              "      <td>25187.0</td>\n",
              "      <td>3521.0</td>\n",
              "      <td>11956.0</td>\n",
              "      <td>3478.0</td>\n",
              "      <td>6.9712</td>\n",
              "      <td>321300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6.0</th>\n",
              "      <td>-117.58</td>\n",
              "      <td>33.85</td>\n",
              "      <td>6.0</td>\n",
              "      <td>16431.0</td>\n",
              "      <td>2640.0</td>\n",
              "      <td>8222.0</td>\n",
              "      <td>2553.0</td>\n",
              "      <td>5.2861</td>\n",
              "      <td>195100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7.0</th>\n",
              "      <td>-121.92</td>\n",
              "      <td>37.53</td>\n",
              "      <td>7.0</td>\n",
              "      <td>28258.0</td>\n",
              "      <td>3864.0</td>\n",
              "      <td>12203.0</td>\n",
              "      <td>3701.0</td>\n",
              "      <td>8.4045</td>\n",
              "      <td>451100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8.0</th>\n",
              "      <td>-117.78</td>\n",
              "      <td>34.03</td>\n",
              "      <td>8.0</td>\n",
              "      <td>32054.0</td>\n",
              "      <td>5290.0</td>\n",
              "      <td>15507.0</td>\n",
              "      <td>5050.0</td>\n",
              "      <td>6.0191</td>\n",
              "      <td>253900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9.0</th>\n",
              "      <td>-118.78</td>\n",
              "      <td>34.16</td>\n",
              "      <td>9.0</td>\n",
              "      <td>30405.0</td>\n",
              "      <td>4093.0</td>\n",
              "      <td>12873.0</td>\n",
              "      <td>3931.0</td>\n",
              "      <td>8.0137</td>\n",
              "      <td>399200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10.0</th>\n",
              "      <td>-117.59</td>\n",
              "      <td>34.13</td>\n",
              "      <td>10.0</td>\n",
              "      <td>20263.0</td>\n",
              "      <td>3915.0</td>\n",
              "      <td>9716.0</td>\n",
              "      <td>3744.0</td>\n",
              "      <td>3.8505</td>\n",
              "      <td>169600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11.0</th>\n",
              "      <td>-121.79</td>\n",
              "      <td>36.64</td>\n",
              "      <td>11.0</td>\n",
              "      <td>32627.0</td>\n",
              "      <td>6445.0</td>\n",
              "      <td>28566.0</td>\n",
              "      <td>6082.0</td>\n",
              "      <td>2.3087</td>\n",
              "      <td>118800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12.0</th>\n",
              "      <td>-118.46</td>\n",
              "      <td>34.40</td>\n",
              "      <td>12.0</td>\n",
              "      <td>25957.0</td>\n",
              "      <td>4798.0</td>\n",
              "      <td>10475.0</td>\n",
              "      <td>4490.0</td>\n",
              "      <td>4.5420</td>\n",
              "      <td>195300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13.0</th>\n",
              "      <td>-117.81</td>\n",
              "      <td>34.08</td>\n",
              "      <td>13.0</td>\n",
              "      <td>18448.0</td>\n",
              "      <td>2474.0</td>\n",
              "      <td>7775.0</td>\n",
              "      <td>2397.0</td>\n",
              "      <td>7.7876</td>\n",
              "      <td>348900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14.0</th>\n",
              "      <td>-117.42</td>\n",
              "      <td>33.35</td>\n",
              "      <td>14.0</td>\n",
              "      <td>25135.0</td>\n",
              "      <td>4819.0</td>\n",
              "      <td>35682.0</td>\n",
              "      <td>4769.0</td>\n",
              "      <td>2.5729</td>\n",
              "      <td>134400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15.0</th>\n",
              "      <td>-117.95</td>\n",
              "      <td>33.98</td>\n",
              "      <td>15.0</td>\n",
              "      <td>16042.0</td>\n",
              "      <td>2602.0</td>\n",
              "      <td>7732.0</td>\n",
              "      <td>2552.0</td>\n",
              "      <td>5.6716</td>\n",
              "      <td>330400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16.0</th>\n",
              "      <td>-117.88</td>\n",
              "      <td>33.96</td>\n",
              "      <td>16.0</td>\n",
              "      <td>19059.0</td>\n",
              "      <td>3079.0</td>\n",
              "      <td>10988.0</td>\n",
              "      <td>3061.0</td>\n",
              "      <td>5.5469</td>\n",
              "      <td>265200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17.0</th>\n",
              "      <td>-117.71</td>\n",
              "      <td>34.15</td>\n",
              "      <td>17.0</td>\n",
              "      <td>17715.0</td>\n",
              "      <td>2370.0</td>\n",
              "      <td>7665.0</td>\n",
              "      <td>2312.0</td>\n",
              "      <td>7.9068</td>\n",
              "      <td>349100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18.0</th>\n",
              "      <td>-119.05</td>\n",
              "      <td>35.33</td>\n",
              "      <td>18.0</td>\n",
              "      <td>12707.0</td>\n",
              "      <td>2685.0</td>\n",
              "      <td>7009.0</td>\n",
              "      <td>2552.0</td>\n",
              "      <td>2.9438</td>\n",
              "      <td>87200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19.0</th>\n",
              "      <td>-118.88</td>\n",
              "      <td>34.02</td>\n",
              "      <td>19.0</td>\n",
              "      <td>15990.0</td>\n",
              "      <td>2611.0</td>\n",
              "      <td>5175.0</td>\n",
              "      <td>2173.0</td>\n",
              "      <td>7.7848</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20.0</th>\n",
              "      <td>-118.52</td>\n",
              "      <td>34.17</td>\n",
              "      <td>20.0</td>\n",
              "      <td>17377.0</td>\n",
              "      <td>4457.0</td>\n",
              "      <td>7450.0</td>\n",
              "      <td>4204.0</td>\n",
              "      <td>3.2154</td>\n",
              "      <td>259600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21.0</th>\n",
              "      <td>-122.46</td>\n",
              "      <td>37.59</td>\n",
              "      <td>21.0</td>\n",
              "      <td>12902.0</td>\n",
              "      <td>2118.0</td>\n",
              "      <td>6160.0</td>\n",
              "      <td>2082.0</td>\n",
              "      <td>5.7653</td>\n",
              "      <td>325800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22.0</th>\n",
              "      <td>-118.42</td>\n",
              "      <td>33.75</td>\n",
              "      <td>22.0</td>\n",
              "      <td>17591.0</td>\n",
              "      <td>2604.0</td>\n",
              "      <td>6897.0</td>\n",
              "      <td>2492.0</td>\n",
              "      <td>8.2831</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23.0</th>\n",
              "      <td>-122.14</td>\n",
              "      <td>37.36</td>\n",
              "      <td>23.0</td>\n",
              "      <td>11294.0</td>\n",
              "      <td>1377.0</td>\n",
              "      <td>3840.0</td>\n",
              "      <td>1367.0</td>\n",
              "      <td>12.1387</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24.0</th>\n",
              "      <td>-117.84</td>\n",
              "      <td>33.82</td>\n",
              "      <td>24.0</td>\n",
              "      <td>10281.0</td>\n",
              "      <td>1689.0</td>\n",
              "      <td>4926.0</td>\n",
              "      <td>1629.0</td>\n",
              "      <td>4.7946</td>\n",
              "      <td>251200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25.0</th>\n",
              "      <td>-118.34</td>\n",
              "      <td>33.78</td>\n",
              "      <td>25.0</td>\n",
              "      <td>11016.0</td>\n",
              "      <td>1626.0</td>\n",
              "      <td>4168.0</td>\n",
              "      <td>1584.0</td>\n",
              "      <td>8.1782</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26.0</th>\n",
              "      <td>-116.97</td>\n",
              "      <td>32.78</td>\n",
              "      <td>26.0</td>\n",
              "      <td>8902.0</td>\n",
              "      <td>1413.0</td>\n",
              "      <td>3941.0</td>\n",
              "      <td>1387.0</td>\n",
              "      <td>4.7943</td>\n",
              "      <td>226900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27.0</th>\n",
              "      <td>-116.94</td>\n",
              "      <td>34.24</td>\n",
              "      <td>27.0</td>\n",
              "      <td>12342.0</td>\n",
              "      <td>2630.0</td>\n",
              "      <td>1300.0</td>\n",
              "      <td>566.0</td>\n",
              "      <td>1.9980</td>\n",
              "      <td>153500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28.0</th>\n",
              "      <td>-118.06</td>\n",
              "      <td>34.13</td>\n",
              "      <td>28.0</td>\n",
              "      <td>12139.0</td>\n",
              "      <td>2873.0</td>\n",
              "      <td>5359.0</td>\n",
              "      <td>2731.0</td>\n",
              "      <td>3.2920</td>\n",
              "      <td>227300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29.0</th>\n",
              "      <td>-120.59</td>\n",
              "      <td>34.70</td>\n",
              "      <td>29.0</td>\n",
              "      <td>17738.0</td>\n",
              "      <td>3114.0</td>\n",
              "      <td>12427.0</td>\n",
              "      <td>2826.0</td>\n",
              "      <td>2.7377</td>\n",
              "      <td>28300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30.0</th>\n",
              "      <td>-118.00</td>\n",
              "      <td>33.97</td>\n",
              "      <td>30.0</td>\n",
              "      <td>6540.0</td>\n",
              "      <td>991.0</td>\n",
              "      <td>3124.0</td>\n",
              "      <td>953.0</td>\n",
              "      <td>6.0663</td>\n",
              "      <td>372600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31.0</th>\n",
              "      <td>-118.48</td>\n",
              "      <td>34.14</td>\n",
              "      <td>31.0</td>\n",
              "      <td>9320.0</td>\n",
              "      <td>1143.0</td>\n",
              "      <td>2980.0</td>\n",
              "      <td>1109.0</td>\n",
              "      <td>10.3599</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32.0</th>\n",
              "      <td>-118.26</td>\n",
              "      <td>34.18</td>\n",
              "      <td>32.0</td>\n",
              "      <td>14556.0</td>\n",
              "      <td>2077.0</td>\n",
              "      <td>5459.0</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>8.1657</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33.0</th>\n",
              "      <td>-122.57</td>\n",
              "      <td>38.02</td>\n",
              "      <td>33.0</td>\n",
              "      <td>9531.0</td>\n",
              "      <td>1487.0</td>\n",
              "      <td>3798.0</td>\n",
              "      <td>1409.0</td>\n",
              "      <td>5.6512</td>\n",
              "      <td>314000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34.0</th>\n",
              "      <td>-119.23</td>\n",
              "      <td>34.46</td>\n",
              "      <td>34.0</td>\n",
              "      <td>9280.0</td>\n",
              "      <td>1765.0</td>\n",
              "      <td>4514.0</td>\n",
              "      <td>1693.0</td>\n",
              "      <td>3.2026</td>\n",
              "      <td>227600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35.0</th>\n",
              "      <td>-121.94</td>\n",
              "      <td>38.27</td>\n",
              "      <td>35.0</td>\n",
              "      <td>10869.0</td>\n",
              "      <td>2226.0</td>\n",
              "      <td>9879.0</td>\n",
              "      <td>2152.0</td>\n",
              "      <td>2.5681</td>\n",
              "      <td>81300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36.0</th>\n",
              "      <td>-118.36</td>\n",
              "      <td>34.13</td>\n",
              "      <td>36.0</td>\n",
              "      <td>6871.0</td>\n",
              "      <td>1180.0</td>\n",
              "      <td>2216.0</td>\n",
              "      <td>1130.0</td>\n",
              "      <td>8.0499</td>\n",
              "      <td>495600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37.0</th>\n",
              "      <td>-118.53</td>\n",
              "      <td>34.09</td>\n",
              "      <td>37.0</td>\n",
              "      <td>5477.0</td>\n",
              "      <td>833.0</td>\n",
              "      <td>1925.0</td>\n",
              "      <td>757.0</td>\n",
              "      <td>8.1888</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38.0</th>\n",
              "      <td>-122.57</td>\n",
              "      <td>37.99</td>\n",
              "      <td>38.0</td>\n",
              "      <td>5587.0</td>\n",
              "      <td>996.0</td>\n",
              "      <td>2466.0</td>\n",
              "      <td>1027.0</td>\n",
              "      <td>4.1711</td>\n",
              "      <td>336900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39.0</th>\n",
              "      <td>-117.37</td>\n",
              "      <td>34.59</td>\n",
              "      <td>39.0</td>\n",
              "      <td>8193.0</td>\n",
              "      <td>1747.0</td>\n",
              "      <td>6852.0</td>\n",
              "      <td>1597.0</td>\n",
              "      <td>2.3832</td>\n",
              "      <td>35000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40.0</th>\n",
              "      <td>-122.61</td>\n",
              "      <td>37.99</td>\n",
              "      <td>40.0</td>\n",
              "      <td>7737.0</td>\n",
              "      <td>1488.0</td>\n",
              "      <td>3108.0</td>\n",
              "      <td>1349.0</td>\n",
              "      <td>4.4375</td>\n",
              "      <td>289600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41.0</th>\n",
              "      <td>-122.13</td>\n",
              "      <td>37.74</td>\n",
              "      <td>41.0</td>\n",
              "      <td>4400.0</td>\n",
              "      <td>666.0</td>\n",
              "      <td>1476.0</td>\n",
              "      <td>648.0</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>248900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42.0</th>\n",
              "      <td>-122.48</td>\n",
              "      <td>37.85</td>\n",
              "      <td>42.0</td>\n",
              "      <td>6297.0</td>\n",
              "      <td>1307.0</td>\n",
              "      <td>2096.0</td>\n",
              "      <td>1205.0</td>\n",
              "      <td>6.4752</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43.0</th>\n",
              "      <td>-118.41</td>\n",
              "      <td>34.06</td>\n",
              "      <td>43.0</td>\n",
              "      <td>4994.0</td>\n",
              "      <td>1057.0</td>\n",
              "      <td>1830.0</td>\n",
              "      <td>969.0</td>\n",
              "      <td>5.5321</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44.0</th>\n",
              "      <td>-122.26</td>\n",
              "      <td>37.50</td>\n",
              "      <td>44.0</td>\n",
              "      <td>6983.0</td>\n",
              "      <td>1131.0</td>\n",
              "      <td>2818.0</td>\n",
              "      <td>1115.0</td>\n",
              "      <td>5.6271</td>\n",
              "      <td>374800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45.0</th>\n",
              "      <td>-118.35</td>\n",
              "      <td>34.07</td>\n",
              "      <td>45.0</td>\n",
              "      <td>7803.0</td>\n",
              "      <td>2154.0</td>\n",
              "      <td>3359.0</td>\n",
              "      <td>2041.0</td>\n",
              "      <td>3.3594</td>\n",
              "      <td>287500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46.0</th>\n",
              "      <td>-122.45</td>\n",
              "      <td>37.74</td>\n",
              "      <td>46.0</td>\n",
              "      <td>6429.0</td>\n",
              "      <td>1093.0</td>\n",
              "      <td>2535.0</td>\n",
              "      <td>1109.0</td>\n",
              "      <td>5.0887</td>\n",
              "      <td>335100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47.0</th>\n",
              "      <td>-122.57</td>\n",
              "      <td>37.97</td>\n",
              "      <td>47.0</td>\n",
              "      <td>5416.0</td>\n",
              "      <td>1115.0</td>\n",
              "      <td>2177.0</td>\n",
              "      <td>1027.0</td>\n",
              "      <td>3.5055</td>\n",
              "      <td>382100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48.0</th>\n",
              "      <td>-122.42</td>\n",
              "      <td>37.79</td>\n",
              "      <td>48.0</td>\n",
              "      <td>4506.0</td>\n",
              "      <td>1342.0</td>\n",
              "      <td>1980.0</td>\n",
              "      <td>1239.0</td>\n",
              "      <td>4.0156</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49.0</th>\n",
              "      <td>-117.10</td>\n",
              "      <td>32.77</td>\n",
              "      <td>49.0</td>\n",
              "      <td>4449.0</td>\n",
              "      <td>711.0</td>\n",
              "      <td>1606.0</td>\n",
              "      <td>709.0</td>\n",
              "      <td>5.7768</td>\n",
              "      <td>281600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50.0</th>\n",
              "      <td>-118.22</td>\n",
              "      <td>34.14</td>\n",
              "      <td>50.0</td>\n",
              "      <td>3657.0</td>\n",
              "      <td>708.0</td>\n",
              "      <td>1725.0</td>\n",
              "      <td>644.0</td>\n",
              "      <td>5.5456</td>\n",
              "      <td>258100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51.0</th>\n",
              "      <td>-121.49</td>\n",
              "      <td>38.55</td>\n",
              "      <td>51.0</td>\n",
              "      <td>4280.0</td>\n",
              "      <td>632.0</td>\n",
              "      <td>1486.0</td>\n",
              "      <td>621.0</td>\n",
              "      <td>5.0359</td>\n",
              "      <td>224100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52.0</th>\n",
              "      <td>-122.41</td>\n",
              "      <td>37.79</td>\n",
              "      <td>52.0</td>\n",
              "      <td>6016.0</td>\n",
              "      <td>2509.0</td>\n",
              "      <td>3436.0</td>\n",
              "      <td>2119.0</td>\n",
              "      <td>2.5166</td>\n",
              "      <td>275000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Another groupby() method worth mentioning is agg(), which lets you run a bunch of different functions on your DataFrame simultaneously. For example, we can generate a simple statistical summary of the dataset as follows:"
      ],
      "metadata": {
        "id": "7vnE4wpiwFMC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.groupby(\"housing_median_age\").agg([len,min,max])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ktV4dIACwGWe",
        "outputId": "0bb5dfba-4b5e-40bd-d081-fa8a93f29d9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   longitude                 latitude                \\\n",
              "                         len     min     max      len    min    max   \n",
              "housing_median_age                                                    \n",
              "1.0                        2 -122.00 -120.93        2  37.65  38.23   \n",
              "2.0                       49 -122.51 -115.80       49  33.16  40.58   \n",
              "3.0                       46 -122.33 -115.60       46  32.87  38.77   \n",
              "4.0                      161 -122.72 -116.76      161  32.65  39.00   \n",
              "5.0                      199 -122.55 -115.55      199  32.55  40.60   \n",
              "6.0                      129 -122.76 -115.52      129  32.67  38.73   \n",
              "7.0                      151 -122.79 -116.31      151  32.54  40.60   \n",
              "8.0                      178 -123.00 -115.59      178  32.55  40.61   \n",
              "9.0                      172 -123.49 -116.22      172  32.56  40.62   \n",
              "10.0                     226 -123.40 -115.52      226  32.57  40.67   \n",
              "11.0                     208 -124.23 -115.51      208  32.58  41.75   \n",
              "12.0                     192 -124.09 -115.53      192  32.58  40.92   \n",
              "13.0                     249 -124.17 -115.50      249  32.58  41.32   \n",
              "14.0                     347 -124.05 -114.57      347  32.57  41.54   \n",
              "15.0                     416 -124.19 -114.31      416  32.55  41.78   \n",
              "16.0                     635 -124.00 -114.60      635  32.56  40.93   \n",
              "17.0                     576 -124.30 -114.56      576  32.56  41.84   \n",
              "18.0                     478 -124.10 -115.22      478  32.56  41.95   \n",
              "19.0                     412 -124.30 -114.47      412  32.59  41.86   \n",
              "20.0                     383 -124.21 -114.57      383  32.56  41.82   \n",
              "21.0                     373 -124.19 -114.60      373  32.57  41.95   \n",
              "22.0                     323 -124.14 -115.55      323  32.56  41.68   \n",
              "23.0                     382 -124.16 -115.37      382  32.56  41.70   \n",
              "24.0                     389 -124.15 -114.73      389  32.60  41.03   \n",
              "25.0                     461 -124.14 -114.58      461  32.57  41.86   \n",
              "26.0                     503 -124.17 -116.00      503  32.57  41.21   \n",
              "27.0                     397 -124.14 -115.54      397  32.58  40.98   \n",
              "28.0                     400 -124.22 -114.65      400  32.58  41.86   \n",
              "29.0                     374 -124.14 -114.58      374  32.58  41.36   \n",
              "30.0                     384 -124.19 -115.37      384  32.59  40.77   \n",
              "31.0                     384 -124.13 -114.61      384  32.63  41.50   \n",
              "32.0                     458 -124.25 -115.37      458  32.58  41.15   \n",
              "33.0                     513 -124.18 -115.46      513  32.60  41.74   \n",
              "34.0                     567 -124.18 -114.59      567  32.58  41.79   \n",
              "35.0                     692 -124.18 -115.38      692  32.67  40.88   \n",
              "36.0                     715 -124.27 -116.56      715  32.58  40.78   \n",
              "37.0                     437 -124.19 -115.60      437  32.62  40.79   \n",
              "38.0                     318 -124.14 -115.38      318  32.64  40.79   \n",
              "39.0                     302 -124.18 -117.07      302  32.69  40.79   \n",
              "40.0                     249 -122.82 -116.98      249  32.63  41.78   \n",
              "41.0                     232 -123.98 -114.59      232  32.68  40.88   \n",
              "42.0                     308 -124.10 -117.07      308  32.70  41.78   \n",
              "43.0                     286 -124.17 -117.01      286  32.66  40.79   \n",
              "44.0                     296 -124.09 -115.73      296  32.69  40.87   \n",
              "45.0                     235 -123.22 -117.12      235  32.69  41.43   \n",
              "46.0                     196 -124.16 -114.60      196  32.66  40.78   \n",
              "47.0                     175 -124.15 -117.10      175  32.68  40.87   \n",
              "48.0                     135 -122.97 -114.61      135  32.69  39.51   \n",
              "49.0                     111 -123.79 -117.10      111  32.68  39.44   \n",
              "50.0                     112 -124.16 -117.11      112  32.70  41.73   \n",
              "51.0                      32 -122.48 -117.15       32  32.72  40.17   \n",
              "52.0                    1052 -124.35 -116.88     1052  32.67  41.32   \n",
              "\n",
              "                   total_rooms                  total_bedrooms  ...  \\\n",
              "                           len     min      max            len  ...   \n",
              "housing_median_age                                              ...   \n",
              "1.0                          2  2062.0   2254.0              2  ...   \n",
              "2.0                         49    96.0  21897.0             49  ...   \n",
              "3.0                         46   475.0  21060.0             46  ...   \n",
              "4.0                        161     2.0  37937.0            161  ...   \n",
              "5.0                        199   111.0  25187.0            199  ...   \n",
              "6.0                        129    78.0  16431.0            129  ...   \n",
              "7.0                        151    77.0  28258.0            151  ...   \n",
              "8.0                        178    32.0  32054.0            178  ...   \n",
              "9.0                        172   254.0  30405.0            172  ...   \n",
              "10.0                       226    69.0  20263.0            226  ...   \n",
              "11.0                       208    72.0  32627.0            208  ...   \n",
              "12.0                       192    57.0  25957.0            192  ...   \n",
              "13.0                       249   221.0  18448.0            249  ...   \n",
              "14.0                       347   231.0  25135.0            347  ...   \n",
              "15.0                       416   208.0  16042.0            416  ...   \n",
              "16.0                       635    18.0  19059.0            635  ...   \n",
              "17.0                       576    89.0  17715.0            576  ...   \n",
              "18.0                       478   102.0  12707.0            478  ...   \n",
              "19.0                       412   121.0  15990.0            412  ...   \n",
              "20.0                       383    37.0  17377.0            383  ...   \n",
              "21.0                       373    44.0  12902.0            373  ...   \n",
              "22.0                       323   197.0  17591.0            323  ...   \n",
              "23.0                       382   149.0  11294.0            382  ...   \n",
              "24.0                       389    84.0  10281.0            389  ...   \n",
              "25.0                       461   182.0  11016.0            461  ...   \n",
              "26.0                       503    15.0   8902.0            503  ...   \n",
              "27.0                       397   210.0  12342.0            397  ...   \n",
              "28.0                       400    26.0  12139.0            400  ...   \n",
              "29.0                       374   124.0  17738.0            374  ...   \n",
              "30.0                       384    48.0   6540.0            384  ...   \n",
              "31.0                       384    40.0   9320.0            384  ...   \n",
              "32.0                       458   296.0  14556.0            458  ...   \n",
              "33.0                       513    32.0   9531.0            513  ...   \n",
              "34.0                       567    45.0   9280.0            567  ...   \n",
              "35.0                       692   167.0  10869.0            692  ...   \n",
              "36.0                       715    15.0   6871.0            715  ...   \n",
              "37.0                       437   102.0   5477.0            437  ...   \n",
              "38.0                       318    51.0   5587.0            318  ...   \n",
              "39.0                       302    47.0   8193.0            302  ...   \n",
              "40.0                       249    67.0   7737.0            249  ...   \n",
              "41.0                       232    80.0   4400.0            232  ...   \n",
              "42.0                       308   106.0   6297.0            308  ...   \n",
              "43.0                       286    88.0   4994.0            286  ...   \n",
              "44.0                       296   121.0   6983.0            296  ...   \n",
              "45.0                       235   121.0   7803.0            235  ...   \n",
              "46.0                       196    11.0   6429.0            196  ...   \n",
              "47.0                       175   463.0   5416.0            175  ...   \n",
              "48.0                       135   143.0   4506.0            135  ...   \n",
              "49.0                       111    98.0   4449.0            111  ...   \n",
              "50.0                       112   187.0   3657.0            112  ...   \n",
              "51.0                        32   175.0   4280.0             32  ...   \n",
              "52.0                      1052     8.0   6016.0           1052  ...   \n",
              "\n",
              "                   population households                median_income          \\\n",
              "                          max        len    min     max           len     min   \n",
              "housing_median_age                                                              \n",
              "1.0                     872.0          2  112.0   268.0             2  4.2500   \n",
              "2.0                    8652.0         49   16.0  2873.0            49  1.9667   \n",
              "3.0                    9623.0         46  123.0  3112.0            46  2.1187   \n",
              "4.0                   16122.0        161    2.0  5189.0           161  0.5360   \n",
              "5.0                   11956.0        199   20.0  3886.0           199  0.7526   \n",
              "6.0                    8222.0        129   10.0  2553.0           129  0.8198   \n",
              "7.0                   15037.0        151   15.0  4072.0           151  1.2667   \n",
              "8.0                   15507.0        178    7.0  5050.0           178  0.8131   \n",
              "9.0                   12873.0        172   29.0  3931.0           172  0.8952   \n",
              "10.0                   9851.0        226   16.0  4616.0           226  0.4999   \n",
              "11.0                  28566.0        208   20.0  6082.0           208  0.9227   \n",
              "12.0                  12153.0        192   15.0  4490.0           192  0.8130   \n",
              "13.0                   7775.0        249   32.0  3232.0           249  0.8288   \n",
              "14.0                  35682.0        347   31.0  4769.0           347  1.1301   \n",
              "15.0                   8997.0        416   20.0  2789.0           416  0.4999   \n",
              "16.0                  10988.0        635    4.0  3595.0           635  0.4999   \n",
              "17.0                   7665.0        576   15.0  3176.0           576  0.6825   \n",
              "18.0                   7009.0        478   21.0  2552.0           478  0.8246   \n",
              "19.0                  11272.0        412   28.0  2905.0           412  0.7025   \n",
              "20.0                   7604.0        383    8.0  4204.0           383  0.8894   \n",
              "21.0                   7282.0        373   27.0  2839.0           373  0.8571   \n",
              "22.0                   9135.0        323   42.0  3522.0           323  0.7473   \n",
              "23.0                   6330.0        382   34.0  1924.0           382  0.4999   \n",
              "24.0                   7174.0        389    8.0  2565.0           389  0.6433   \n",
              "25.0                   7679.0        461   16.0  2902.0           461  0.7990   \n",
              "26.0                   8551.0        503    3.0  1822.0           503  0.5360   \n",
              "27.0                   5023.0        397   44.0  1763.0           397  0.8543   \n",
              "28.0                   6912.0        400    9.0  2731.0           400  0.4999   \n",
              "29.0                  12427.0        374   18.0  2826.0           374  0.7069   \n",
              "30.0                   4480.0        384   13.0  1536.0           384  0.7007   \n",
              "31.0                   5666.0        384    7.0  1755.0           384  0.7684   \n",
              "32.0                   5459.0        458   52.0  2017.0           458  0.8984   \n",
              "33.0                   5650.0        513   10.0  1537.0           513  0.5360   \n",
              "34.0                   5477.0        567   13.0  1789.0           567  0.8907   \n",
              "35.0                   9879.0        692   29.0  2769.0           692  0.7075   \n",
              "36.0                   4711.0        715    2.0  2323.0           715  0.4999   \n",
              "37.0                   3895.0        437   14.0  1874.0           437  0.6831   \n",
              "38.0                   3702.0        318   10.0  1253.0           318  0.7683   \n",
              "39.0                   6852.0        302   23.0  1597.0           302  0.7160   \n",
              "40.0                   4828.0        249   32.0  2028.0           249  0.9234   \n",
              "41.0                   3121.0        232   19.0  1030.0           232  0.9782   \n",
              "42.0                   6846.0        308   25.0  2156.0           308  0.8991   \n",
              "43.0                   3920.0        286   13.0  1429.0           286  0.4999   \n",
              "44.0                   3913.0        296   30.0  1383.0           296  1.1576   \n",
              "45.0                   4715.0        235   27.0  2041.0           235  0.9337   \n",
              "46.0                   7443.0        196    5.0  2406.0           196  0.4999   \n",
              "47.0                   3348.0        175   86.0  1106.0           175  1.1868   \n",
              "48.0                   3298.0        135   50.0  1239.0           135  0.8024   \n",
              "49.0                   2862.0        111   23.0  1009.0           111  0.7068   \n",
              "50.0                   3080.0        112   35.0   997.0           112  1.2041   \n",
              "51.0                   1795.0         32   55.0   621.0            32  1.2852   \n",
              "52.0                   4518.0       1052    1.0  2538.0          1052  0.4999   \n",
              "\n",
              "                            median_house_value                      \n",
              "                        max                len       min       max  \n",
              "housing_median_age                                                  \n",
              "1.0                  5.2636                  2  189200.0  191300.0  \n",
              "2.0                 10.1531                 49   47500.0  500001.0  \n",
              "3.0                 11.5199                 46   83200.0  500001.0  \n",
              "4.0                 13.4883                161   42500.0  500001.0  \n",
              "5.0                 12.6320                199   50000.0  500001.0  \n",
              "6.0                 13.0502                129   58000.0  500001.0  \n",
              "7.0                 13.8093                151   56200.0  500001.0  \n",
              "8.0                 14.9009                178   22500.0  500001.0  \n",
              "9.0                 10.8289                172   54200.0  500001.0  \n",
              "10.0                12.5420                226   53300.0  500001.0  \n",
              "11.0                13.1477                208   50400.0  500001.0  \n",
              "12.0                10.0760                192   40000.0  500001.0  \n",
              "13.0                 9.8346                249   43000.0  500001.0  \n",
              "14.0                15.0001                347   32900.0  500001.0  \n",
              "15.0                15.0001                416   32500.0  500001.0  \n",
              "16.0                12.0088                635   14999.0  500001.0  \n",
              "17.0                15.0001                576   27500.0  500001.0  \n",
              "18.0                11.6017                478   47500.0  500001.0  \n",
              "19.0                15.0000                412   14999.0  500001.0  \n",
              "20.0                12.2092                383   32500.0  500001.0  \n",
              "21.0                11.6833                373   25000.0  500001.0  \n",
              "22.0                11.2463                323   34400.0  500001.0  \n",
              "23.0                12.9792                382   30000.0  500001.0  \n",
              "24.0                14.5833                389   32500.0  500001.0  \n",
              "25.0                13.3913                461   45500.0  500001.0  \n",
              "26.0                15.0001                503   36600.0  500001.0  \n",
              "27.0                15.0001                397   40000.0  500001.0  \n",
              "28.0                14.4113                400   40900.0  500001.0  \n",
              "29.0                13.9470                374   28300.0  500001.0  \n",
              "30.0                12.4429                384   42500.0  500001.0  \n",
              "31.0                15.0001                384   34200.0  500001.0  \n",
              "32.0                15.0001                458   43100.0  500001.0  \n",
              "33.0                15.0001                513   22500.0  500001.0  \n",
              "34.0                15.0001                567   26600.0  500001.0  \n",
              "35.0                15.0001                692   39600.0  500001.0  \n",
              "36.0                15.0001                715   14999.0  500001.0  \n",
              "37.0                13.3670                437   42100.0  500001.0  \n",
              "38.0                12.7823                318   39400.0  500001.0  \n",
              "39.0                11.8603                302   17500.0  500001.0  \n",
              "40.0                15.0001                249   43000.0  500001.0  \n",
              "41.0                 8.8619                232   41800.0  500001.0  \n",
              "42.0                15.0001                308   39400.0  500001.0  \n",
              "43.0                15.0001                286   46600.0  500001.0  \n",
              "44.0                12.4237                296   48300.0  500001.0  \n",
              "45.0                15.0001                235   30000.0  500001.0  \n",
              "46.0                15.0001                196   26900.0  500001.0  \n",
              "47.0                 9.4590                175   49600.0  500001.0  \n",
              "48.0                15.0001                135   48600.0  500001.0  \n",
              "49.0                12.9591                111   52000.0  500001.0  \n",
              "50.0                13.3036                112   54200.0  500001.0  \n",
              "51.0                 9.3694                 32   51900.0  500001.0  \n",
              "52.0                15.0001               1052   14999.0  500001.0  \n",
              "\n",
              "[52 rows x 24 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bd3454a8-a888-4259-bb75-486b11d56614\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"3\" halign=\"left\">longitude</th>\n",
              "      <th colspan=\"3\" halign=\"left\">latitude</th>\n",
              "      <th colspan=\"3\" halign=\"left\">total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>...</th>\n",
              "      <th>population</th>\n",
              "      <th colspan=\"3\" halign=\"left\">households</th>\n",
              "      <th colspan=\"3\" halign=\"left\">median_income</th>\n",
              "      <th colspan=\"3\" halign=\"left\">median_house_value</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>len</th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "      <th>len</th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "      <th>len</th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "      <th>len</th>\n",
              "      <th>...</th>\n",
              "      <th>max</th>\n",
              "      <th>len</th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "      <th>len</th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "      <th>len</th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>housing_median_age</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",
              "      <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",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>2</td>\n",
              "      <td>-122.00</td>\n",
              "      <td>-120.93</td>\n",
              "      <td>2</td>\n",
              "      <td>37.65</td>\n",
              "      <td>38.23</td>\n",
              "      <td>2</td>\n",
              "      <td>2062.0</td>\n",
              "      <td>2254.0</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>872.0</td>\n",
              "      <td>2</td>\n",
              "      <td>112.0</td>\n",
              "      <td>268.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4.2500</td>\n",
              "      <td>5.2636</td>\n",
              "      <td>2</td>\n",
              "      <td>189200.0</td>\n",
              "      <td>191300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>49</td>\n",
              "      <td>-122.51</td>\n",
              "      <td>-115.80</td>\n",
              "      <td>49</td>\n",
              "      <td>33.16</td>\n",
              "      <td>40.58</td>\n",
              "      <td>49</td>\n",
              "      <td>96.0</td>\n",
              "      <td>21897.0</td>\n",
              "      <td>49</td>\n",
              "      <td>...</td>\n",
              "      <td>8652.0</td>\n",
              "      <td>49</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2873.0</td>\n",
              "      <td>49</td>\n",
              "      <td>1.9667</td>\n",
              "      <td>10.1531</td>\n",
              "      <td>49</td>\n",
              "      <td>47500.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <td>46</td>\n",
              "      <td>-122.33</td>\n",
              "      <td>-115.60</td>\n",
              "      <td>46</td>\n",
              "      <td>32.87</td>\n",
              "      <td>38.77</td>\n",
              "      <td>46</td>\n",
              "      <td>475.0</td>\n",
              "      <td>21060.0</td>\n",
              "      <td>46</td>\n",
              "      <td>...</td>\n",
              "      <td>9623.0</td>\n",
              "      <td>46</td>\n",
              "      <td>123.0</td>\n",
              "      <td>3112.0</td>\n",
              "      <td>46</td>\n",
              "      <td>2.1187</td>\n",
              "      <td>11.5199</td>\n",
              "      <td>46</td>\n",
              "      <td>83200.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.0</th>\n",
              "      <td>161</td>\n",
              "      <td>-122.72</td>\n",
              "      <td>-116.76</td>\n",
              "      <td>161</td>\n",
              "      <td>32.65</td>\n",
              "      <td>39.00</td>\n",
              "      <td>161</td>\n",
              "      <td>2.0</td>\n",
              "      <td>37937.0</td>\n",
              "      <td>161</td>\n",
              "      <td>...</td>\n",
              "      <td>16122.0</td>\n",
              "      <td>161</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5189.0</td>\n",
              "      <td>161</td>\n",
              "      <td>0.5360</td>\n",
              "      <td>13.4883</td>\n",
              "      <td>161</td>\n",
              "      <td>42500.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.0</th>\n",
              "      <td>199</td>\n",
              "      <td>-122.55</td>\n",
              "      <td>-115.55</td>\n",
              "      <td>199</td>\n",
              "      <td>32.55</td>\n",
              "      <td>40.60</td>\n",
              "      <td>199</td>\n",
              "      <td>111.0</td>\n",
              "      <td>25187.0</td>\n",
              "      <td>199</td>\n",
              "      <td>...</td>\n",
              "      <td>11956.0</td>\n",
              "      <td>199</td>\n",
              "      <td>20.0</td>\n",
              "      <td>3886.0</td>\n",
              "      <td>199</td>\n",
              "      <td>0.7526</td>\n",
              "      <td>12.6320</td>\n",
              "      <td>199</td>\n",
              "      <td>50000.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6.0</th>\n",
              "      <td>129</td>\n",
              "      <td>-122.76</td>\n",
              "      <td>-115.52</td>\n",
              "      <td>129</td>\n",
              "      <td>32.67</td>\n",
              "      <td>38.73</td>\n",
              "      <td>129</td>\n",
              "      <td>78.0</td>\n",
              "      <td>16431.0</td>\n",
              "      <td>129</td>\n",
              "      <td>...</td>\n",
              "      <td>8222.0</td>\n",
              "      <td>129</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2553.0</td>\n",
              "      <td>129</td>\n",
              "      <td>0.8198</td>\n",
              "      <td>13.0502</td>\n",
              "      <td>129</td>\n",
              "      <td>58000.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7.0</th>\n",
              "      <td>151</td>\n",
              "      <td>-122.79</td>\n",
              "      <td>-116.31</td>\n",
              "      <td>151</td>\n",
              "      <td>32.54</td>\n",
              "      <td>40.60</td>\n",
              "      <td>151</td>\n",
              "      <td>77.0</td>\n",
              "      <td>28258.0</td>\n",
              "      <td>151</td>\n",
              "      <td>...</td>\n",
              "      <td>15037.0</td>\n",
              "      <td>151</td>\n",
              "      <td>15.0</td>\n",
              "      <td>4072.0</td>\n",
              "      <td>151</td>\n",
              "      <td>1.2667</td>\n",
              "      <td>13.8093</td>\n",
              "      <td>151</td>\n",
              "      <td>56200.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8.0</th>\n",
              "      <td>178</td>\n",
              "      <td>-123.00</td>\n",
              "      <td>-115.59</td>\n",
              "      <td>178</td>\n",
              "      <td>32.55</td>\n",
              "      <td>40.61</td>\n",
              "      <td>178</td>\n",
              "      <td>32.0</td>\n",
              "      <td>32054.0</td>\n",
              "      <td>178</td>\n",
              "      <td>...</td>\n",
              "      <td>15507.0</td>\n",
              "      <td>178</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5050.0</td>\n",
              "      <td>178</td>\n",
              "      <td>0.8131</td>\n",
              "      <td>14.9009</td>\n",
              "      <td>178</td>\n",
              "      <td>22500.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9.0</th>\n",
              "      <td>172</td>\n",
              "      <td>-123.49</td>\n",
              "      <td>-116.22</td>\n",
              "      <td>172</td>\n",
              "      <td>32.56</td>\n",
              "      <td>40.62</td>\n",
              "      <td>172</td>\n",
              "      <td>254.0</td>\n",
              "      <td>30405.0</td>\n",
              "      <td>172</td>\n",
              "      <td>...</td>\n",
              "      <td>12873.0</td>\n",
              "      <td>172</td>\n",
              "      <td>29.0</td>\n",
              "      <td>3931.0</td>\n",
              "      <td>172</td>\n",
              "      <td>0.8952</td>\n",
              "      <td>10.8289</td>\n",
              "      <td>172</td>\n",
              "      <td>54200.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10.0</th>\n",
              "      <td>226</td>\n",
              "      <td>-123.40</td>\n",
              "      <td>-115.52</td>\n",
              "      <td>226</td>\n",
              "      <td>32.57</td>\n",
              "      <td>40.67</td>\n",
              "      <td>226</td>\n",
              "      <td>69.0</td>\n",
              "      <td>20263.0</td>\n",
              "      <td>226</td>\n",
              "      <td>...</td>\n",
              "      <td>9851.0</td>\n",
              "      <td>226</td>\n",
              "      <td>16.0</td>\n",
              "      <td>4616.0</td>\n",
              "      <td>226</td>\n",
              "      <td>0.4999</td>\n",
              "      <td>12.5420</td>\n",
              "      <td>226</td>\n",
              "      <td>53300.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11.0</th>\n",
              "      <td>208</td>\n",
              "      <td>-124.23</td>\n",
              "      <td>-115.51</td>\n",
              "      <td>208</td>\n",
              "      <td>32.58</td>\n",
              "      <td>41.75</td>\n",
              "      <td>208</td>\n",
              "      <td>72.0</td>\n",
              "      <td>32627.0</td>\n",
              "      <td>208</td>\n",
              "      <td>...</td>\n",
              "      <td>28566.0</td>\n",
              "      <td>208</td>\n",
              "      <td>20.0</td>\n",
              "      <td>6082.0</td>\n",
              "      <td>208</td>\n",
              "      <td>0.9227</td>\n",
              "      <td>13.1477</td>\n",
              "      <td>208</td>\n",
              "      <td>50400.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12.0</th>\n",
              "      <td>192</td>\n",
              "      <td>-124.09</td>\n",
              "      <td>-115.53</td>\n",
              "      <td>192</td>\n",
              "      <td>32.58</td>\n",
              "      <td>40.92</td>\n",
              "      <td>192</td>\n",
              "      <td>57.0</td>\n",
              "      <td>25957.0</td>\n",
              "      <td>192</td>\n",
              "      <td>...</td>\n",
              "      <td>12153.0</td>\n",
              "      <td>192</td>\n",
              "      <td>15.0</td>\n",
              "      <td>4490.0</td>\n",
              "      <td>192</td>\n",
              "      <td>0.8130</td>\n",
              "      <td>10.0760</td>\n",
              "      <td>192</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13.0</th>\n",
              "      <td>249</td>\n",
              "      <td>-124.17</td>\n",
              "      <td>-115.50</td>\n",
              "      <td>249</td>\n",
              "      <td>32.58</td>\n",
              "      <td>41.32</td>\n",
              "      <td>249</td>\n",
              "      <td>221.0</td>\n",
              "      <td>18448.0</td>\n",
              "      <td>249</td>\n",
              "      <td>...</td>\n",
              "      <td>7775.0</td>\n",
              "      <td>249</td>\n",
              "      <td>32.0</td>\n",
              "      <td>3232.0</td>\n",
              "      <td>249</td>\n",
              "      <td>0.8288</td>\n",
              "      <td>9.8346</td>\n",
              "      <td>249</td>\n",
              "      <td>43000.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14.0</th>\n",
              "      <td>347</td>\n",
              "      <td>-124.05</td>\n",
              "      <td>-114.57</td>\n",
              "      <td>347</td>\n",
              "      <td>32.57</td>\n",
              "      <td>41.54</td>\n",
              "      <td>347</td>\n",
              "      <td>231.0</td>\n",
              "      <td>25135.0</td>\n",
              "      <td>347</td>\n",
              "      <td>...</td>\n",
              "      <td>35682.0</td>\n",
              "      <td>347</td>\n",
              "      <td>31.0</td>\n",
              "      <td>4769.0</td>\n",
              "      <td>347</td>\n",
              "      <td>1.1301</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>347</td>\n",
              "      <td>32900.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15.0</th>\n",
              "      <td>416</td>\n",
              "      <td>-124.19</td>\n",
              "      <td>-114.31</td>\n",
              "      <td>416</td>\n",
              "      <td>32.55</td>\n",
              "      <td>41.78</td>\n",
              "      <td>416</td>\n",
              "      <td>208.0</td>\n",
              "      <td>16042.0</td>\n",
              "      <td>416</td>\n",
              "      <td>...</td>\n",
              "      <td>8997.0</td>\n",
              "      <td>416</td>\n",
              "      <td>20.0</td>\n",
              "      <td>2789.0</td>\n",
              "      <td>416</td>\n",
              "      <td>0.4999</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>416</td>\n",
              "      <td>32500.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16.0</th>\n",
              "      <td>635</td>\n",
              "      <td>-124.00</td>\n",
              "      <td>-114.60</td>\n",
              "      <td>635</td>\n",
              "      <td>32.56</td>\n",
              "      <td>40.93</td>\n",
              "      <td>635</td>\n",
              "      <td>18.0</td>\n",
              "      <td>19059.0</td>\n",
              "      <td>635</td>\n",
              "      <td>...</td>\n",
              "      <td>10988.0</td>\n",
              "      <td>635</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3595.0</td>\n",
              "      <td>635</td>\n",
              "      <td>0.4999</td>\n",
              "      <td>12.0088</td>\n",
              "      <td>635</td>\n",
              "      <td>14999.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17.0</th>\n",
              "      <td>576</td>\n",
              "      <td>-124.30</td>\n",
              "      <td>-114.56</td>\n",
              "      <td>576</td>\n",
              "      <td>32.56</td>\n",
              "      <td>41.84</td>\n",
              "      <td>576</td>\n",
              "      <td>89.0</td>\n",
              "      <td>17715.0</td>\n",
              "      <td>576</td>\n",
              "      <td>...</td>\n",
              "      <td>7665.0</td>\n",
              "      <td>576</td>\n",
              "      <td>15.0</td>\n",
              "      <td>3176.0</td>\n",
              "      <td>576</td>\n",
              "      <td>0.6825</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>576</td>\n",
              "      <td>27500.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18.0</th>\n",
              "      <td>478</td>\n",
              "      <td>-124.10</td>\n",
              "      <td>-115.22</td>\n",
              "      <td>478</td>\n",
              "      <td>32.56</td>\n",
              "      <td>41.95</td>\n",
              "      <td>478</td>\n",
              "      <td>102.0</td>\n",
              "      <td>12707.0</td>\n",
              "      <td>478</td>\n",
              "      <td>...</td>\n",
              "      <td>7009.0</td>\n",
              "      <td>478</td>\n",
              "      <td>21.0</td>\n",
              "      <td>2552.0</td>\n",
              "      <td>478</td>\n",
              "      <td>0.8246</td>\n",
              "      <td>11.6017</td>\n",
              "      <td>478</td>\n",
              "      <td>47500.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19.0</th>\n",
              "      <td>412</td>\n",
              "      <td>-124.30</td>\n",
              "      <td>-114.47</td>\n",
              "      <td>412</td>\n",
              "      <td>32.59</td>\n",
              "      <td>41.86</td>\n",
              "      <td>412</td>\n",
              "      <td>121.0</td>\n",
              "      <td>15990.0</td>\n",
              "      <td>412</td>\n",
              "      <td>...</td>\n",
              "      <td>11272.0</td>\n",
              "      <td>412</td>\n",
              "      <td>28.0</td>\n",
              "      <td>2905.0</td>\n",
              "      <td>412</td>\n",
              "      <td>0.7025</td>\n",
              "      <td>15.0000</td>\n",
              "      <td>412</td>\n",
              "      <td>14999.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20.0</th>\n",
              "      <td>383</td>\n",
              "      <td>-124.21</td>\n",
              "      <td>-114.57</td>\n",
              "      <td>383</td>\n",
              "      <td>32.56</td>\n",
              "      <td>41.82</td>\n",
              "      <td>383</td>\n",
              "      <td>37.0</td>\n",
              "      <td>17377.0</td>\n",
              "      <td>383</td>\n",
              "      <td>...</td>\n",
              "      <td>7604.0</td>\n",
              "      <td>383</td>\n",
              "      <td>8.0</td>\n",
              "      <td>4204.0</td>\n",
              "      <td>383</td>\n",
              "      <td>0.8894</td>\n",
              "      <td>12.2092</td>\n",
              "      <td>383</td>\n",
              "      <td>32500.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21.0</th>\n",
              "      <td>373</td>\n",
              "      <td>-124.19</td>\n",
              "      <td>-114.60</td>\n",
              "      <td>373</td>\n",
              "      <td>32.57</td>\n",
              "      <td>41.95</td>\n",
              "      <td>373</td>\n",
              "      <td>44.0</td>\n",
              "      <td>12902.0</td>\n",
              "      <td>373</td>\n",
              "      <td>...</td>\n",
              "      <td>7282.0</td>\n",
              "      <td>373</td>\n",
              "      <td>27.0</td>\n",
              "      <td>2839.0</td>\n",
              "      <td>373</td>\n",
              "      <td>0.8571</td>\n",
              "      <td>11.6833</td>\n",
              "      <td>373</td>\n",
              "      <td>25000.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22.0</th>\n",
              "      <td>323</td>\n",
              "      <td>-124.14</td>\n",
              "      <td>-115.55</td>\n",
              "      <td>323</td>\n",
              "      <td>32.56</td>\n",
              "      <td>41.68</td>\n",
              "      <td>323</td>\n",
              "      <td>197.0</td>\n",
              "      <td>17591.0</td>\n",
              "      <td>323</td>\n",
              "      <td>...</td>\n",
              "      <td>9135.0</td>\n",
              "      <td>323</td>\n",
              "      <td>42.0</td>\n",
              "      <td>3522.0</td>\n",
              "      <td>323</td>\n",
              "      <td>0.7473</td>\n",
              "      <td>11.2463</td>\n",
              "      <td>323</td>\n",
              "      <td>34400.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23.0</th>\n",
              "      <td>382</td>\n",
              "      <td>-124.16</td>\n",
              "      <td>-115.37</td>\n",
              "      <td>382</td>\n",
              "      <td>32.56</td>\n",
              "      <td>41.70</td>\n",
              "      <td>382</td>\n",
              "      <td>149.0</td>\n",
              "      <td>11294.0</td>\n",
              "      <td>382</td>\n",
              "      <td>...</td>\n",
              "      <td>6330.0</td>\n",
              "      <td>382</td>\n",
              "      <td>34.0</td>\n",
              "      <td>1924.0</td>\n",
              "      <td>382</td>\n",
              "      <td>0.4999</td>\n",
              "      <td>12.9792</td>\n",
              "      <td>382</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24.0</th>\n",
              "      <td>389</td>\n",
              "      <td>-124.15</td>\n",
              "      <td>-114.73</td>\n",
              "      <td>389</td>\n",
              "      <td>32.60</td>\n",
              "      <td>41.03</td>\n",
              "      <td>389</td>\n",
              "      <td>84.0</td>\n",
              "      <td>10281.0</td>\n",
              "      <td>389</td>\n",
              "      <td>...</td>\n",
              "      <td>7174.0</td>\n",
              "      <td>389</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2565.0</td>\n",
              "      <td>389</td>\n",
              "      <td>0.6433</td>\n",
              "      <td>14.5833</td>\n",
              "      <td>389</td>\n",
              "      <td>32500.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25.0</th>\n",
              "      <td>461</td>\n",
              "      <td>-124.14</td>\n",
              "      <td>-114.58</td>\n",
              "      <td>461</td>\n",
              "      <td>32.57</td>\n",
              "      <td>41.86</td>\n",
              "      <td>461</td>\n",
              "      <td>182.0</td>\n",
              "      <td>11016.0</td>\n",
              "      <td>461</td>\n",
              "      <td>...</td>\n",
              "      <td>7679.0</td>\n",
              "      <td>461</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2902.0</td>\n",
              "      <td>461</td>\n",
              "      <td>0.7990</td>\n",
              "      <td>13.3913</td>\n",
              "      <td>461</td>\n",
              "      <td>45500.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26.0</th>\n",
              "      <td>503</td>\n",
              "      <td>-124.17</td>\n",
              "      <td>-116.00</td>\n",
              "      <td>503</td>\n",
              "      <td>32.57</td>\n",
              "      <td>41.21</td>\n",
              "      <td>503</td>\n",
              "      <td>15.0</td>\n",
              "      <td>8902.0</td>\n",
              "      <td>503</td>\n",
              "      <td>...</td>\n",
              "      <td>8551.0</td>\n",
              "      <td>503</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1822.0</td>\n",
              "      <td>503</td>\n",
              "      <td>0.5360</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>503</td>\n",
              "      <td>36600.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27.0</th>\n",
              "      <td>397</td>\n",
              "      <td>-124.14</td>\n",
              "      <td>-115.54</td>\n",
              "      <td>397</td>\n",
              "      <td>32.58</td>\n",
              "      <td>40.98</td>\n",
              "      <td>397</td>\n",
              "      <td>210.0</td>\n",
              "      <td>12342.0</td>\n",
              "      <td>397</td>\n",
              "      <td>...</td>\n",
              "      <td>5023.0</td>\n",
              "      <td>397</td>\n",
              "      <td>44.0</td>\n",
              "      <td>1763.0</td>\n",
              "      <td>397</td>\n",
              "      <td>0.8543</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>397</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28.0</th>\n",
              "      <td>400</td>\n",
              "      <td>-124.22</td>\n",
              "      <td>-114.65</td>\n",
              "      <td>400</td>\n",
              "      <td>32.58</td>\n",
              "      <td>41.86</td>\n",
              "      <td>400</td>\n",
              "      <td>26.0</td>\n",
              "      <td>12139.0</td>\n",
              "      <td>400</td>\n",
              "      <td>...</td>\n",
              "      <td>6912.0</td>\n",
              "      <td>400</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2731.0</td>\n",
              "      <td>400</td>\n",
              "      <td>0.4999</td>\n",
              "      <td>14.4113</td>\n",
              "      <td>400</td>\n",
              "      <td>40900.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29.0</th>\n",
              "      <td>374</td>\n",
              "      <td>-124.14</td>\n",
              "      <td>-114.58</td>\n",
              "      <td>374</td>\n",
              "      <td>32.58</td>\n",
              "      <td>41.36</td>\n",
              "      <td>374</td>\n",
              "      <td>124.0</td>\n",
              "      <td>17738.0</td>\n",
              "      <td>374</td>\n",
              "      <td>...</td>\n",
              "      <td>12427.0</td>\n",
              "      <td>374</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2826.0</td>\n",
              "      <td>374</td>\n",
              "      <td>0.7069</td>\n",
              "      <td>13.9470</td>\n",
              "      <td>374</td>\n",
              "      <td>28300.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30.0</th>\n",
              "      <td>384</td>\n",
              "      <td>-124.19</td>\n",
              "      <td>-115.37</td>\n",
              "      <td>384</td>\n",
              "      <td>32.59</td>\n",
              "      <td>40.77</td>\n",
              "      <td>384</td>\n",
              "      <td>48.0</td>\n",
              "      <td>6540.0</td>\n",
              "      <td>384</td>\n",
              "      <td>...</td>\n",
              "      <td>4480.0</td>\n",
              "      <td>384</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1536.0</td>\n",
              "      <td>384</td>\n",
              "      <td>0.7007</td>\n",
              "      <td>12.4429</td>\n",
              "      <td>384</td>\n",
              "      <td>42500.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31.0</th>\n",
              "      <td>384</td>\n",
              "      <td>-124.13</td>\n",
              "      <td>-114.61</td>\n",
              "      <td>384</td>\n",
              "      <td>32.63</td>\n",
              "      <td>41.50</td>\n",
              "      <td>384</td>\n",
              "      <td>40.0</td>\n",
              "      <td>9320.0</td>\n",
              "      <td>384</td>\n",
              "      <td>...</td>\n",
              "      <td>5666.0</td>\n",
              "      <td>384</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1755.0</td>\n",
              "      <td>384</td>\n",
              "      <td>0.7684</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>384</td>\n",
              "      <td>34200.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32.0</th>\n",
              "      <td>458</td>\n",
              "      <td>-124.25</td>\n",
              "      <td>-115.37</td>\n",
              "      <td>458</td>\n",
              "      <td>32.58</td>\n",
              "      <td>41.15</td>\n",
              "      <td>458</td>\n",
              "      <td>296.0</td>\n",
              "      <td>14556.0</td>\n",
              "      <td>458</td>\n",
              "      <td>...</td>\n",
              "      <td>5459.0</td>\n",
              "      <td>458</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>458</td>\n",
              "      <td>0.8984</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>458</td>\n",
              "      <td>43100.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33.0</th>\n",
              "      <td>513</td>\n",
              "      <td>-124.18</td>\n",
              "      <td>-115.46</td>\n",
              "      <td>513</td>\n",
              "      <td>32.60</td>\n",
              "      <td>41.74</td>\n",
              "      <td>513</td>\n",
              "      <td>32.0</td>\n",
              "      <td>9531.0</td>\n",
              "      <td>513</td>\n",
              "      <td>...</td>\n",
              "      <td>5650.0</td>\n",
              "      <td>513</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1537.0</td>\n",
              "      <td>513</td>\n",
              "      <td>0.5360</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>513</td>\n",
              "      <td>22500.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34.0</th>\n",
              "      <td>567</td>\n",
              "      <td>-124.18</td>\n",
              "      <td>-114.59</td>\n",
              "      <td>567</td>\n",
              "      <td>32.58</td>\n",
              "      <td>41.79</td>\n",
              "      <td>567</td>\n",
              "      <td>45.0</td>\n",
              "      <td>9280.0</td>\n",
              "      <td>567</td>\n",
              "      <td>...</td>\n",
              "      <td>5477.0</td>\n",
              "      <td>567</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1789.0</td>\n",
              "      <td>567</td>\n",
              "      <td>0.8907</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>567</td>\n",
              "      <td>26600.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35.0</th>\n",
              "      <td>692</td>\n",
              "      <td>-124.18</td>\n",
              "      <td>-115.38</td>\n",
              "      <td>692</td>\n",
              "      <td>32.67</td>\n",
              "      <td>40.88</td>\n",
              "      <td>692</td>\n",
              "      <td>167.0</td>\n",
              "      <td>10869.0</td>\n",
              "      <td>692</td>\n",
              "      <td>...</td>\n",
              "      <td>9879.0</td>\n",
              "      <td>692</td>\n",
              "      <td>29.0</td>\n",
              "      <td>2769.0</td>\n",
              "      <td>692</td>\n",
              "      <td>0.7075</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>692</td>\n",
              "      <td>39600.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36.0</th>\n",
              "      <td>715</td>\n",
              "      <td>-124.27</td>\n",
              "      <td>-116.56</td>\n",
              "      <td>715</td>\n",
              "      <td>32.58</td>\n",
              "      <td>40.78</td>\n",
              "      <td>715</td>\n",
              "      <td>15.0</td>\n",
              "      <td>6871.0</td>\n",
              "      <td>715</td>\n",
              "      <td>...</td>\n",
              "      <td>4711.0</td>\n",
              "      <td>715</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2323.0</td>\n",
              "      <td>715</td>\n",
              "      <td>0.4999</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>715</td>\n",
              "      <td>14999.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37.0</th>\n",
              "      <td>437</td>\n",
              "      <td>-124.19</td>\n",
              "      <td>-115.60</td>\n",
              "      <td>437</td>\n",
              "      <td>32.62</td>\n",
              "      <td>40.79</td>\n",
              "      <td>437</td>\n",
              "      <td>102.0</td>\n",
              "      <td>5477.0</td>\n",
              "      <td>437</td>\n",
              "      <td>...</td>\n",
              "      <td>3895.0</td>\n",
              "      <td>437</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1874.0</td>\n",
              "      <td>437</td>\n",
              "      <td>0.6831</td>\n",
              "      <td>13.3670</td>\n",
              "      <td>437</td>\n",
              "      <td>42100.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38.0</th>\n",
              "      <td>318</td>\n",
              "      <td>-124.14</td>\n",
              "      <td>-115.38</td>\n",
              "      <td>318</td>\n",
              "      <td>32.64</td>\n",
              "      <td>40.79</td>\n",
              "      <td>318</td>\n",
              "      <td>51.0</td>\n",
              "      <td>5587.0</td>\n",
              "      <td>318</td>\n",
              "      <td>...</td>\n",
              "      <td>3702.0</td>\n",
              "      <td>318</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1253.0</td>\n",
              "      <td>318</td>\n",
              "      <td>0.7683</td>\n",
              "      <td>12.7823</td>\n",
              "      <td>318</td>\n",
              "      <td>39400.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39.0</th>\n",
              "      <td>302</td>\n",
              "      <td>-124.18</td>\n",
              "      <td>-117.07</td>\n",
              "      <td>302</td>\n",
              "      <td>32.69</td>\n",
              "      <td>40.79</td>\n",
              "      <td>302</td>\n",
              "      <td>47.0</td>\n",
              "      <td>8193.0</td>\n",
              "      <td>302</td>\n",
              "      <td>...</td>\n",
              "      <td>6852.0</td>\n",
              "      <td>302</td>\n",
              "      <td>23.0</td>\n",
              "      <td>1597.0</td>\n",
              "      <td>302</td>\n",
              "      <td>0.7160</td>\n",
              "      <td>11.8603</td>\n",
              "      <td>302</td>\n",
              "      <td>17500.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40.0</th>\n",
              "      <td>249</td>\n",
              "      <td>-122.82</td>\n",
              "      <td>-116.98</td>\n",
              "      <td>249</td>\n",
              "      <td>32.63</td>\n",
              "      <td>41.78</td>\n",
              "      <td>249</td>\n",
              "      <td>67.0</td>\n",
              "      <td>7737.0</td>\n",
              "      <td>249</td>\n",
              "      <td>...</td>\n",
              "      <td>4828.0</td>\n",
              "      <td>249</td>\n",
              "      <td>32.0</td>\n",
              "      <td>2028.0</td>\n",
              "      <td>249</td>\n",
              "      <td>0.9234</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>249</td>\n",
              "      <td>43000.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41.0</th>\n",
              "      <td>232</td>\n",
              "      <td>-123.98</td>\n",
              "      <td>-114.59</td>\n",
              "      <td>232</td>\n",
              "      <td>32.68</td>\n",
              "      <td>40.88</td>\n",
              "      <td>232</td>\n",
              "      <td>80.0</td>\n",
              "      <td>4400.0</td>\n",
              "      <td>232</td>\n",
              "      <td>...</td>\n",
              "      <td>3121.0</td>\n",
              "      <td>232</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1030.0</td>\n",
              "      <td>232</td>\n",
              "      <td>0.9782</td>\n",
              "      <td>8.8619</td>\n",
              "      <td>232</td>\n",
              "      <td>41800.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42.0</th>\n",
              "      <td>308</td>\n",
              "      <td>-124.10</td>\n",
              "      <td>-117.07</td>\n",
              "      <td>308</td>\n",
              "      <td>32.70</td>\n",
              "      <td>41.78</td>\n",
              "      <td>308</td>\n",
              "      <td>106.0</td>\n",
              "      <td>6297.0</td>\n",
              "      <td>308</td>\n",
              "      <td>...</td>\n",
              "      <td>6846.0</td>\n",
              "      <td>308</td>\n",
              "      <td>25.0</td>\n",
              "      <td>2156.0</td>\n",
              "      <td>308</td>\n",
              "      <td>0.8991</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>308</td>\n",
              "      <td>39400.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43.0</th>\n",
              "      <td>286</td>\n",
              "      <td>-124.17</td>\n",
              "      <td>-117.01</td>\n",
              "      <td>286</td>\n",
              "      <td>32.66</td>\n",
              "      <td>40.79</td>\n",
              "      <td>286</td>\n",
              "      <td>88.0</td>\n",
              "      <td>4994.0</td>\n",
              "      <td>286</td>\n",
              "      <td>...</td>\n",
              "      <td>3920.0</td>\n",
              "      <td>286</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1429.0</td>\n",
              "      <td>286</td>\n",
              "      <td>0.4999</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>286</td>\n",
              "      <td>46600.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44.0</th>\n",
              "      <td>296</td>\n",
              "      <td>-124.09</td>\n",
              "      <td>-115.73</td>\n",
              "      <td>296</td>\n",
              "      <td>32.69</td>\n",
              "      <td>40.87</td>\n",
              "      <td>296</td>\n",
              "      <td>121.0</td>\n",
              "      <td>6983.0</td>\n",
              "      <td>296</td>\n",
              "      <td>...</td>\n",
              "      <td>3913.0</td>\n",
              "      <td>296</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1383.0</td>\n",
              "      <td>296</td>\n",
              "      <td>1.1576</td>\n",
              "      <td>12.4237</td>\n",
              "      <td>296</td>\n",
              "      <td>48300.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45.0</th>\n",
              "      <td>235</td>\n",
              "      <td>-123.22</td>\n",
              "      <td>-117.12</td>\n",
              "      <td>235</td>\n",
              "      <td>32.69</td>\n",
              "      <td>41.43</td>\n",
              "      <td>235</td>\n",
              "      <td>121.0</td>\n",
              "      <td>7803.0</td>\n",
              "      <td>235</td>\n",
              "      <td>...</td>\n",
              "      <td>4715.0</td>\n",
              "      <td>235</td>\n",
              "      <td>27.0</td>\n",
              "      <td>2041.0</td>\n",
              "      <td>235</td>\n",
              "      <td>0.9337</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>235</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46.0</th>\n",
              "      <td>196</td>\n",
              "      <td>-124.16</td>\n",
              "      <td>-114.60</td>\n",
              "      <td>196</td>\n",
              "      <td>32.66</td>\n",
              "      <td>40.78</td>\n",
              "      <td>196</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6429.0</td>\n",
              "      <td>196</td>\n",
              "      <td>...</td>\n",
              "      <td>7443.0</td>\n",
              "      <td>196</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2406.0</td>\n",
              "      <td>196</td>\n",
              "      <td>0.4999</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>196</td>\n",
              "      <td>26900.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47.0</th>\n",
              "      <td>175</td>\n",
              "      <td>-124.15</td>\n",
              "      <td>-117.10</td>\n",
              "      <td>175</td>\n",
              "      <td>32.68</td>\n",
              "      <td>40.87</td>\n",
              "      <td>175</td>\n",
              "      <td>463.0</td>\n",
              "      <td>5416.0</td>\n",
              "      <td>175</td>\n",
              "      <td>...</td>\n",
              "      <td>3348.0</td>\n",
              "      <td>175</td>\n",
              "      <td>86.0</td>\n",
              "      <td>1106.0</td>\n",
              "      <td>175</td>\n",
              "      <td>1.1868</td>\n",
              "      <td>9.4590</td>\n",
              "      <td>175</td>\n",
              "      <td>49600.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48.0</th>\n",
              "      <td>135</td>\n",
              "      <td>-122.97</td>\n",
              "      <td>-114.61</td>\n",
              "      <td>135</td>\n",
              "      <td>32.69</td>\n",
              "      <td>39.51</td>\n",
              "      <td>135</td>\n",
              "      <td>143.0</td>\n",
              "      <td>4506.0</td>\n",
              "      <td>135</td>\n",
              "      <td>...</td>\n",
              "      <td>3298.0</td>\n",
              "      <td>135</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1239.0</td>\n",
              "      <td>135</td>\n",
              "      <td>0.8024</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>135</td>\n",
              "      <td>48600.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49.0</th>\n",
              "      <td>111</td>\n",
              "      <td>-123.79</td>\n",
              "      <td>-117.10</td>\n",
              "      <td>111</td>\n",
              "      <td>32.68</td>\n",
              "      <td>39.44</td>\n",
              "      <td>111</td>\n",
              "      <td>98.0</td>\n",
              "      <td>4449.0</td>\n",
              "      <td>111</td>\n",
              "      <td>...</td>\n",
              "      <td>2862.0</td>\n",
              "      <td>111</td>\n",
              "      <td>23.0</td>\n",
              "      <td>1009.0</td>\n",
              "      <td>111</td>\n",
              "      <td>0.7068</td>\n",
              "      <td>12.9591</td>\n",
              "      <td>111</td>\n",
              "      <td>52000.0</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50.0</th>\n",
              "      <td>112</td>\n",
              "      <td>-124.16</td>\n",
              "      <td>-117.11</td>\n",
              "      <td>112</td>\n",
              "      <td>32.70</td>\n",
              "      <td>41.73</td>\n",
              "      <td>112</td>\n",
              "      <td>187.0</td>\n",
              "      <td>3657.0</td>\n",
              "      <td>112</td>\n",
              "      <td>...</td>\n",
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              "      <td>35.0</td>\n",
              "      <td>997.0</td>\n",
              "      <td>112</td>\n",
              "      <td>1.2041</td>\n",
              "      <td>13.3036</td>\n",
              "      <td>112</td>\n",
              "      <td>54200.0</td>\n",
              "      <td>500001.0</td>\n",
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              "      <td>32</td>\n",
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              "      <td>4280.0</td>\n",
              "      <td>32</td>\n",
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              "      <td>32</td>\n",
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              "      <td>51900.0</td>\n",
              "      <td>500001.0</td>\n",
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              "    <tr>\n",
              "      <th>52.0</th>\n",
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              "      <td>8.0</td>\n",
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              "      <td>1052</td>\n",
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              "      <td>15.0001</td>\n",
              "      <td>1052</td>\n",
              "      <td>14999.0</td>\n",
              "      <td>500001.0</td>\n",
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      "source": [
        "#Multi-indexes\n",
        "---\n",
        "---\n",
        "In all of the examples we've seen thus far we've been working with DataFrame or Series objects with a single-label index. groupby() is slightly different in the fact that, depending on the operation we run, it will sometimes result in what is called a multi-index.\n",
        "\n",
        "A multi-index differs from a regular index in that it has multiple levels. For example:"
      ],
      "metadata": {
        "id": "EA3AztdDxfPc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.groupby([\"longitude\", \"latitude\"]).population.describe()\n",
        "\n",
        "# Raw DF\n",
        "# housing_data.groupby([\"longitude\", \"latitude\"]).apply(lambda df: df.loc[:,[\"longitude\",\"housing_median_age\",\"population\"]])"
      ],
      "metadata": {
        "colab": {
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          "height": 455
        },
        "id": "8pC3IiYixi4A",
        "outputId": "10478cb3-711e-4c93-f8d2-d1659bde28a4"
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      "execution_count": null,
      "outputs": [
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              "                    count    mean  std     min     25%     50%     75%     max\n",
              "longitude latitude                                                            \n",
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Multi-indices have several methods for dealing with their tiered structure which are absent for single-level indices. They also require two levels of labels to retrieve a value. Dealing with multi-index output is a common \"gotcha\" for users new to pandas.\n",
        "\n",
        "However, in general the multi-index method you will use most often is the one for converting back to a regular index, the reset_index() method:"
      ],
      "metadata": {
        "id": "ks0z0v7s1P6d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.groupby([\"longitude\", \"latitude\"]).population.describe().reset_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "aMh50ocB1YDv",
        "outputId": "adb80bd5-022a-4cda-b847-d00d5a9fa9ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "       longitude  latitude  count    mean  std     min     25%     50%  \\\n",
              "0        -124.35     40.54    1.0   806.0  NaN   806.0   806.0   806.0   \n",
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              "...          ...       ...    ...     ...  ...     ...     ...     ...   \n",
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          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Sorting\n",
        "---\n",
        "---\n",
        "\n",
        "Once the data is single indexed it is easier to find it among a dataframe. To get data sorted we can sort it ourselves by using **sort_values()** methord."
      ],
      "metadata": {
        "id": "RWqgrUxI2HaW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "located_housing_data = housing_data.groupby([\"longitude\", \"latitude\"]).describe()"
      ],
      "metadata": {
        "id": "mqVoDXqp2Gad"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "SERIES/DF.sort_value(by=\"key\", ascending=True)\n",
        "\n",
        "→ key = single , list (if multiple sorting citereon), tuple (if inheriting columns)"
      ],
      "metadata": {
        "id": "K9tb9Oat8T7q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# reset_index() then sort housing median age by count\n",
        "#located_housing_data.reset_index().housing_median_age.sort_values(by=\"count\")\n",
        "\n",
        "# sort housing median age by count\n",
        "#located_housing_data.housing_median_age.sort_values(by=\"count\")\n",
        "\n",
        "#decending\n",
        "located_housing_data.housing_median_age.sort_values(by=\"count\", ascending=False)\n"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "HxjVqtJz3IO8",
        "outputId": "22b05696-8a56-47d1-9db1-276264033406"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "                    count       mean        std   min    25%   50%   75%   max\n",
              "longitude latitude                                                            \n",
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              "      <td>19.0</td>\n",
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              "    <tr>\n",
              "      <th>40.36</th>\n",
              "      <td>1.0</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19.00</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>-120.70</th>\n",
              "      <th>35.28</th>\n",
              "      <td>1.0</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.00</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>-114.31</th>\n",
              "      <th>34.19</th>\n",
              "      <td>1.0</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15.0</td>\n",
              "      <td>15.00</td>\n",
              "      <td>15.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11054 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2d8ed4a2-a816-4e27-8126-ed3c06957f19')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2d8ed4a2-a816-4e27-8126-ed3c06957f19 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2d8ed4a2-a816-4e27-8126-ed3c06957f19');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ".sort_index() ==> resets according to index (default)"
      ],
      "metadata": {
        "id": "ZTOD2o5X8k3k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Dtypes\n",
        "---\n",
        "---\n",
        "The data type for a column in a DataFrame or a Series is known as the dtype.\n",
        "\n",
        "You can use the dtype property to grab the type of a specific column.\n",
        ">>DF.dtypes\n",
        "\n",
        ">>SERIES.dtype"
      ],
      "metadata": {
        "id": "s5T1MPsVCqdk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mVnKzRN4DN0z",
        "outputId": "b6986e21-b4eb-4833-ffd0-d80fde1e8e18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "longitude             float64\n",
              "latitude              float64\n",
              "housing_median_age    float64\n",
              "total_rooms           float64\n",
              "total_bedrooms        float64\n",
              "population            float64\n",
              "households            float64\n",
              "median_income         float64\n",
              "median_house_value    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.population.dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nDEwdIG3Cwf3",
        "outputId": "e6484786-e952-4c87-d874-3109e1815449"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('float64')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data types tell us something about how pandas is storing the data internally. **float64** means that it's using a 64-bit floating point number; **int64** means a similarly sized integer instead, and so on.\n",
        "\n",
        "One peculiarity to keep in mind (and on display very clearly here) is that columns consisting entirely of **strings** do not get their own type; they are instead given the **object** type.\n",
        "\n",
        "It's possible to convert a column of one type into another wherever such a conversion makes sense by using the **`astype()`** function. For example, we may transform the points column from its existing int64 data type into a **float64** data type:"
      ],
      "metadata": {
        "id": "QzqdbsXPDZwE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.population.astype('int64')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M1icQlYuDuTT",
        "outputId": "45f01e01-1c89-4b52-c023-0352913b9075"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        1015\n",
              "1        1129\n",
              "2         333\n",
              "3         515\n",
              "4         624\n",
              "         ... \n",
              "16995     907\n",
              "16996    1194\n",
              "16997    1244\n",
              "16998    1298\n",
              "16999     806\n",
              "Name: population, Length: 17000, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A DataFrame or Series index has its own dtype, too:"
      ],
      "metadata": {
        "id": "qINjc09jD6jv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.index.dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CuOkTC9ID50L",
        "outputId": "e31544a2-2e63-46cd-a255-26b5704167fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('int64')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Missing data\n",
        "Entries missing values are given the value **NaN**, short for \"Not a Number\". For technical reasons these NaN values are always of the float64 dtype. \n",
        "\n",
        "Pandas provides some methods specific to missing data. To select NaN entries you can use `pd.isnull()` (or its companion `pd.notnull()`).\n",
        "\n",
        "Replacing missing values is a common operation. Pandas provides a really handy method for this problem: fillna(). 4\n",
        "\n",
        "`fillna()` provides a few different strategies for mitigating such data. For example, we can simply replace each NaN with an \"Unknown\":\n",
        "\n",
        ">DF/SERIES.`fillna`(\"Replacing TEXT\")\n",
        "\n",
        "Or we could fill each missing value with the first non-null value that appears sometime after the given record in the database. This is known as the backfill strategy.\n",
        "\n",
        ">DF/SERIES.`bfill`(inplace=False)"
      ],
      "metadata": {
        "id": "26lMHfxrFC69"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Replace\n",
        "\n",
        "Alternatively, we may have a non-null value that we would like to replace.\n",
        "\n",
        ">DF/SERIES.`replace`(\"to_replace\", \"replaced_with\")"
      ],
      "metadata": {
        "id": "pXPi0A-uHZuK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Renaming\n",
        "---\n",
        "---\n",
        "The first function we'll introduce here is `rename()`, which lets you change index names and/or column names. \n",
        "\n",
        "`rename()` lets you rename index or column values by specifying a **index** or **column** keyword parameter, respectively. It supports a variety of input formats, but usually a Python dictionary is the most convenient. Here is an example using it to rename some elements of the index."
      ],
      "metadata": {
        "id": "UxoQSl9t0zw2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.rename(index={0: \"first_entry\", 1: \"second_entry\"})\n",
        "\n",
        "#You'll probably rename columns very often, but rename index values very rarely. For that, set_index() is usually more convenient."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "dcN8D-wq1z0D",
        "outputId": "eb9100d6-d152-4d7f-f988-073820bbad7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              longitude  latitude  housing_median_age  total_rooms  \\\n",
              "first_entry     -114.31     34.19                15.0       5612.0   \n",
              "second_entry    -114.47     34.40                19.0       7650.0   \n",
              "2               -114.56     33.69                17.0        720.0   \n",
              "3               -114.57     33.64                14.0       1501.0   \n",
              "4               -114.57     33.57                20.0       1454.0   \n",
              "...                 ...       ...                 ...          ...   \n",
              "16995           -124.26     40.58                52.0       2217.0   \n",
              "16996           -124.27     40.69                36.0       2349.0   \n",
              "16997           -124.30     41.84                17.0       2677.0   \n",
              "16998           -124.30     41.80                19.0       2672.0   \n",
              "16999           -124.35     40.54                52.0       1820.0   \n",
              "\n",
              "              total_bedrooms  population  households  median_income  \\\n",
              "first_entry           1283.0      1015.0       472.0         1.4936   \n",
              "second_entry          1901.0      1129.0       463.0         1.8200   \n",
              "2                      174.0       333.0       117.0         1.6509   \n",
              "3                      337.0       515.0       226.0         3.1917   \n",
              "4                      326.0       624.0       262.0         1.9250   \n",
              "...                      ...         ...         ...            ...   \n",
              "16995                  394.0       907.0       369.0         2.3571   \n",
              "16996                  528.0      1194.0       465.0         2.5179   \n",
              "16997                  531.0      1244.0       456.0         3.0313   \n",
              "16998                  552.0      1298.0       478.0         1.9797   \n",
              "16999                  300.0       806.0       270.0         3.0147   \n",
              "\n",
              "              median_house_value  \n",
              "first_entry              66900.0  \n",
              "second_entry             80100.0  \n",
              "2                        85700.0  \n",
              "3                        73400.0  \n",
              "4                        65500.0  \n",
              "...                          ...  \n",
              "16995                   111400.0  \n",
              "16996                    79000.0  \n",
              "16997                   103600.0  \n",
              "16998                    85800.0  \n",
              "16999                    94600.0  \n",
              "\n",
              "[17000 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ea2a7dca-3f83-442d-b668-c5321becec9b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
              "    .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>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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>first_entry</th>\n",
              "      <td>-114.31</td>\n",
              "      <td>34.19</td>\n",
              "      <td>15.0</td>\n",
              "      <td>5612.0</td>\n",
              "      <td>1283.0</td>\n",
              "      <td>1015.0</td>\n",
              "      <td>472.0</td>\n",
              "      <td>1.4936</td>\n",
              "      <td>66900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>second_entry</th>\n",
              "      <td>-114.47</td>\n",
              "      <td>34.40</td>\n",
              "      <td>19.0</td>\n",
              "      <td>7650.0</td>\n",
              "      <td>1901.0</td>\n",
              "      <td>1129.0</td>\n",
              "      <td>463.0</td>\n",
              "      <td>1.8200</td>\n",
              "      <td>80100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-114.56</td>\n",
              "      <td>33.69</td>\n",
              "      <td>17.0</td>\n",
              "      <td>720.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>333.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>1.6509</td>\n",
              "      <td>85700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.64</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1501.0</td>\n",
              "      <td>337.0</td>\n",
              "      <td>515.0</td>\n",
              "      <td>226.0</td>\n",
              "      <td>3.1917</td>\n",
              "      <td>73400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.57</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1454.0</td>\n",
              "      <td>326.0</td>\n",
              "      <td>624.0</td>\n",
              "      <td>262.0</td>\n",
              "      <td>1.9250</td>\n",
              "      <td>65500.0</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>16995</th>\n",
              "      <td>-124.26</td>\n",
              "      <td>40.58</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2217.0</td>\n",
              "      <td>394.0</td>\n",
              "      <td>907.0</td>\n",
              "      <td>369.0</td>\n",
              "      <td>2.3571</td>\n",
              "      <td>111400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16996</th>\n",
              "      <td>-124.27</td>\n",
              "      <td>40.69</td>\n",
              "      <td>36.0</td>\n",
              "      <td>2349.0</td>\n",
              "      <td>528.0</td>\n",
              "      <td>1194.0</td>\n",
              "      <td>465.0</td>\n",
              "      <td>2.5179</td>\n",
              "      <td>79000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16997</th>\n",
              "      <td>-124.30</td>\n",
              "      <td>41.84</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2677.0</td>\n",
              "      <td>531.0</td>\n",
              "      <td>1244.0</td>\n",
              "      <td>456.0</td>\n",
              "      <td>3.0313</td>\n",
              "      <td>103600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16998</th>\n",
              "      <td>-124.30</td>\n",
              "      <td>41.80</td>\n",
              "      <td>19.0</td>\n",
              "      <td>2672.0</td>\n",
              "      <td>552.0</td>\n",
              "      <td>1298.0</td>\n",
              "      <td>478.0</td>\n",
              "      <td>1.9797</td>\n",
              "      <td>85800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16999</th>\n",
              "      <td>-124.35</td>\n",
              "      <td>40.54</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1820.0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>806.0</td>\n",
              "      <td>270.0</td>\n",
              "      <td>3.0147</td>\n",
              "      <td>94600.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17000 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ea2a7dca-3f83-442d-b668-c5321becec9b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "housing_data.rename(columns={\"population\":\"number_of_people\"})"
      ],
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        },
        "id": "qXlb1VYt07Jr",
        "outputId": "7f2d5530-7ecf-4630-8d5d-36629843a0bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0        -114.31     34.19                15.0       5612.0          1283.0   \n",
              "1        -114.47     34.40                19.0       7650.0          1901.0   \n",
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              "16999    -124.35     40.54                52.0       1820.0           300.0   \n",
              "\n",
              "       number_of_people  households  median_income  median_house_value  \n",
              "0                1015.0       472.0         1.4936             66900.0  \n",
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              "...                 ...         ...            ...                 ...  \n",
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              "16998            1298.0       478.0         1.9797             85800.0  \n",
              "16999             806.0       270.0         3.0147             94600.0  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "rename_axis() \n",
        "\n",
        "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rename_axis.html"
      ],
      "metadata": {
        "id": "beKQ_mVI3ygG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Combining\n",
        "---\n",
        "---\n",
        "When performing operations on a dataset, we will sometimes need to combine different DataFrames and/or Series in non-trivial ways. Pandas has three core methods for doing this. In order of increasing complexity, these are `concat()`, `join()`, and `merge()`. Most of what merge() can do can also be done more simply with join(), so we will omit it and focus on the first two functions here.\n",
        "\n",
        "The simplest combining method is **concat()**. Given a list of elements, this function will smush those elements together along an axis.\n",
        "\n",
        "This is useful when we have data in different DataFrame or Series objects but having the same fields (columns)."
      ],
      "metadata": {
        "id": "gGKo0pyd4TJO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_training_data = pd.read_csv(\"/content/sample_data/california_housing_train.csv\")\n",
        "housing_test_data = pd.read_csv(\"/content/sample_data/california_housing_test.csv\")\n",
        "\n",
        "pd.concat([housing_training_data,housing_test_data])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "zwvSZy9N4iAq",
        "outputId": "482554d5-152e-49ab-f5dc-796868c6a9b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0       -114.31     34.19                15.0       5612.0          1283.0   \n",
              "1       -114.47     34.40                19.0       7650.0          1901.0   \n",
              "2       -114.56     33.69                17.0        720.0           174.0   \n",
              "3       -114.57     33.64                14.0       1501.0           337.0   \n",
              "4       -114.57     33.57                20.0       1454.0           326.0   \n",
              "...         ...       ...                 ...          ...             ...   \n",
              "2995    -119.86     34.42                23.0       1450.0           642.0   \n",
              "2996    -118.14     34.06                27.0       5257.0          1082.0   \n",
              "2997    -119.70     36.30                10.0        956.0           201.0   \n",
              "2998    -117.12     34.10                40.0         96.0            14.0   \n",
              "2999    -119.63     34.42                42.0       1765.0           263.0   \n",
              "\n",
              "      population  households  median_income  median_house_value  \n",
              "0         1015.0       472.0         1.4936             66900.0  \n",
              "1         1129.0       463.0         1.8200             80100.0  \n",
              "2          333.0       117.0         1.6509             85700.0  \n",
              "3          515.0       226.0         3.1917             73400.0  \n",
              "4          624.0       262.0         1.9250             65500.0  \n",
              "...          ...         ...            ...                 ...  \n",
              "2995      1258.0       607.0         1.1790            225000.0  \n",
              "2996      3496.0      1036.0         3.3906            237200.0  \n",
              "2997       693.0       220.0         2.2895             62000.0  \n",
              "2998        46.0        14.0         3.2708            162500.0  \n",
              "2999       753.0       260.0         8.5608            500001.0  \n",
              "\n",
              "[20000 rows x 9 columns]"
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              "      <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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-114.31</td>\n",
              "      <td>34.19</td>\n",
              "      <td>15.0</td>\n",
              "      <td>5612.0</td>\n",
              "      <td>1283.0</td>\n",
              "      <td>1015.0</td>\n",
              "      <td>472.0</td>\n",
              "      <td>1.4936</td>\n",
              "      <td>66900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-114.47</td>\n",
              "      <td>34.40</td>\n",
              "      <td>19.0</td>\n",
              "      <td>7650.0</td>\n",
              "      <td>1901.0</td>\n",
              "      <td>1129.0</td>\n",
              "      <td>463.0</td>\n",
              "      <td>1.8200</td>\n",
              "      <td>80100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-114.56</td>\n",
              "      <td>33.69</td>\n",
              "      <td>17.0</td>\n",
              "      <td>720.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>333.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>1.6509</td>\n",
              "      <td>85700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.64</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1501.0</td>\n",
              "      <td>337.0</td>\n",
              "      <td>515.0</td>\n",
              "      <td>226.0</td>\n",
              "      <td>3.1917</td>\n",
              "      <td>73400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.57</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1454.0</td>\n",
              "      <td>326.0</td>\n",
              "      <td>624.0</td>\n",
              "      <td>262.0</td>\n",
              "      <td>1.9250</td>\n",
              "      <td>65500.0</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>2995</th>\n",
              "      <td>-119.86</td>\n",
              "      <td>34.42</td>\n",
              "      <td>23.0</td>\n",
              "      <td>1450.0</td>\n",
              "      <td>642.0</td>\n",
              "      <td>1258.0</td>\n",
              "      <td>607.0</td>\n",
              "      <td>1.1790</td>\n",
              "      <td>225000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2996</th>\n",
              "      <td>-118.14</td>\n",
              "      <td>34.06</td>\n",
              "      <td>27.0</td>\n",
              "      <td>5257.0</td>\n",
              "      <td>1082.0</td>\n",
              "      <td>3496.0</td>\n",
              "      <td>1036.0</td>\n",
              "      <td>3.3906</td>\n",
              "      <td>237200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2997</th>\n",
              "      <td>-119.70</td>\n",
              "      <td>36.30</td>\n",
              "      <td>10.0</td>\n",
              "      <td>956.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>693.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>2.2895</td>\n",
              "      <td>62000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2998</th>\n",
              "      <td>-117.12</td>\n",
              "      <td>34.10</td>\n",
              "      <td>40.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>3.2708</td>\n",
              "      <td>162500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2999</th>\n",
              "      <td>-119.63</td>\n",
              "      <td>34.42</td>\n",
              "      <td>42.0</td>\n",
              "      <td>1765.0</td>\n",
              "      <td>263.0</td>\n",
              "      <td>753.0</td>\n",
              "      <td>260.0</td>\n",
              "      <td>8.5608</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20000 rows × 9 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "join() lets you combine different DataFrame objects which have an index in common. "
      ],
      "metadata": {
        "id": "sF-PayPN57CI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "left = housing_training_data.set_index([\"longitude\",\"latitude\"])\n",
        "right = housing_test_data.set_index([\"longitude\",\"latitude\"])\n",
        "\n",
        "left.join(right, lsuffix=\"_train\", rsuffix=\"_test\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "gi7NwuBF52w9",
        "outputId": "68bbdc10-63b2-473b-a4dd-96f302b2a343"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    housing_median_age_train  total_rooms_train  \\\n",
              "longitude latitude                                                \n",
              "-124.35   40.54                         52.0             1820.0   \n",
              "-124.30   41.80                         19.0             2672.0   \n",
              "          41.84                         17.0             2677.0   \n",
              "-124.27   40.69                         36.0             2349.0   \n",
              "-124.26   40.58                         52.0             2217.0   \n",
              "...                                      ...                ...   \n",
              "-114.57   33.57                         20.0             1454.0   \n",
              "          33.64                         14.0             1501.0   \n",
              "-114.56   33.69                         17.0              720.0   \n",
              "-114.47   34.40                         19.0             7650.0   \n",
              "-114.31   34.19                         15.0             5612.0   \n",
              "\n",
              "                    total_bedrooms_train  population_train  households_train  \\\n",
              "longitude latitude                                                             \n",
              "-124.35   40.54                    300.0             806.0             270.0   \n",
              "-124.30   41.80                    552.0            1298.0             478.0   \n",
              "          41.84                    531.0            1244.0             456.0   \n",
              "-124.27   40.69                    528.0            1194.0             465.0   \n",
              "-124.26   40.58                    394.0             907.0             369.0   \n",
              "...                                  ...               ...               ...   \n",
              "-114.57   33.57                    326.0             624.0             262.0   \n",
              "          33.64                    337.0             515.0             226.0   \n",
              "-114.56   33.69                    174.0             333.0             117.0   \n",
              "-114.47   34.40                   1901.0            1129.0             463.0   \n",
              "-114.31   34.19                   1283.0            1015.0             472.0   \n",
              "\n",
              "                    median_income_train  median_house_value_train  \\\n",
              "longitude latitude                                                  \n",
              "-124.35   40.54                  3.0147                   94600.0   \n",
              "-124.30   41.80                  1.9797                   85800.0   \n",
              "          41.84                  3.0313                  103600.0   \n",
              "-124.27   40.69                  2.5179                   79000.0   \n",
              "-124.26   40.58                  2.3571                  111400.0   \n",
              "...                                 ...                       ...   \n",
              "-114.57   33.57                  1.9250                   65500.0   \n",
              "          33.64                  3.1917                   73400.0   \n",
              "-114.56   33.69                  1.6509                   85700.0   \n",
              "-114.47   34.40                  1.8200                   80100.0   \n",
              "-114.31   34.19                  1.4936                   66900.0   \n",
              "\n",
              "                    housing_median_age_test  total_rooms_test  \\\n",
              "longitude latitude                                              \n",
              "-124.35   40.54                         NaN               NaN   \n",
              "-124.30   41.80                         NaN               NaN   \n",
              "          41.84                         NaN               NaN   \n",
              "-124.27   40.69                         NaN               NaN   \n",
              "-124.26   40.58                         NaN               NaN   \n",
              "...                                     ...               ...   \n",
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              "          33.64                         NaN               NaN   \n",
              "-114.56   33.69                         NaN               NaN   \n",
              "-114.47   34.40                         NaN               NaN   \n",
              "-114.31   34.19                         NaN               NaN   \n",
              "\n",
              "                    total_bedrooms_test  population_test  households_test  \\\n",
              "longitude latitude                                                          \n",
              "-124.35   40.54                     NaN              NaN              NaN   \n",
              "-124.30   41.80                     NaN              NaN              NaN   \n",
              "          41.84                     NaN              NaN              NaN   \n",
              "-124.27   40.69                     NaN              NaN              NaN   \n",
              "-124.26   40.58                     NaN              NaN              NaN   \n",
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              "          33.64                     NaN              NaN              NaN   \n",
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              "-114.47   34.40                     NaN              NaN              NaN   \n",
              "-114.31   34.19                     NaN              NaN              NaN   \n",
              "\n",
              "                    median_income_test  median_house_value_test  \n",
              "longitude latitude                                               \n",
              "-124.35   40.54                    NaN                      NaN  \n",
              "-124.30   41.80                    NaN                      NaN  \n",
              "          41.84                    NaN                      NaN  \n",
              "-124.27   40.69                    NaN                      NaN  \n",
              "-124.26   40.58                    NaN                      NaN  \n",
              "...                                ...                      ...  \n",
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              "      <th>housing_median_age_train</th>\n",
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              "      <th>longitude</th>\n",
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              "      <th rowspan=\"2\" valign=\"top\">-124.30</th>\n",
              "      <th>41.80</th>\n",
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              "      <td>1298.0</td>\n",
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              "      <th>41.84</th>\n",
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              "      <td>1244.0</td>\n",
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              "      <td>103600.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>40.69</th>\n",
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              "      <td>528.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>-124.26</th>\n",
              "      <th>40.58</th>\n",
              "      <td>52.0</td>\n",
              "      <td>2217.0</td>\n",
              "      <td>394.0</td>\n",
              "      <td>907.0</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">-114.57</th>\n",
              "      <th>33.57</th>\n",
              "      <td>20.0</td>\n",
              "      <td>1454.0</td>\n",
              "      <td>326.0</td>\n",
              "      <td>624.0</td>\n",
              "      <td>262.0</td>\n",
              "      <td>1.9250</td>\n",
              "      <td>65500.0</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>33.64</th>\n",
              "      <td>14.0</td>\n",
              "      <td>1501.0</td>\n",
              "      <td>337.0</td>\n",
              "      <td>515.0</td>\n",
              "      <td>226.0</td>\n",
              "      <td>3.1917</td>\n",
              "      <td>73400.0</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>-114.56</th>\n",
              "      <th>33.69</th>\n",
              "      <td>17.0</td>\n",
              "      <td>720.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>333.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>1.6509</td>\n",
              "      <td>85700.0</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>-114.47</th>\n",
              "      <th>34.40</th>\n",
              "      <td>19.0</td>\n",
              "      <td>7650.0</td>\n",
              "      <td>1901.0</td>\n",
              "      <td>1129.0</td>\n",
              "      <td>463.0</td>\n",
              "      <td>1.8200</td>\n",
              "      <td>80100.0</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>-114.31</th>\n",
              "      <th>34.19</th>\n",
              "      <td>15.0</td>\n",
              "      <td>5612.0</td>\n",
              "      <td>1283.0</td>\n",
              "      <td>1015.0</td>\n",
              "      <td>472.0</td>\n",
              "      <td>1.4936</td>\n",
              "      <td>66900.0</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",
              "  </tbody>\n",
              "</table>\n",
              "<p>17543 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d9a8f7bc-31ac-43ef-8429-288b04682e3b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d9a8f7bc-31ac-43ef-8429-288b04682e3b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d9a8f7bc-31ac-43ef-8429-288b04682e3b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Must Look:\n",
        "\n",
        "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html"
      ],
      "metadata": {
        "id": "bziLvQW37D-A"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ktTyJ1-s7GrF"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}