5_01_Intro_To_Pandas.ipynb

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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/edwardoughton/spatial_computing/blob/main/5_01_Intro_To_Pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Intro to Pandas\n",
        "\n",
        "In this class we will introduce the `pandas` package, and cover a range of basic functions necessary to get started with this very useful and flexible package.\n",
        "\n",
        "The Python data analysis package `pandas` is widely used for importing, manipulating and exporting datasets in a panel format: https://pandas.pydata.org/\n",
        "\n",
        "By 'panel format' we mean that `pandas` is based on using dataframes. They allow us to store data as panels in rows and columns. You may have encountered this data structure in R or Matlab. If not, you can think of the approach as similar to how you use a spreadsheet (except with far greater flexibility and power in Python).\n",
        "\n",
        "Later, we will work with an affiliated sub-library `geopandas` that specifically enables you to work with geospatial data.\n",
        "\n",
        "**For the purposes of learning as much as possible in this class, please begin by turning off code completions (see Tools/Settings/Editor/untick automatically trigger code completions).**"
      ],
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      "source": [
        "## Importing data into Colab\n",
        "\n",
        "To begin, we first need to import data into our colab notebook. This will be a common first step before you begin quantitatively analyzing data.\n",
        "\n",
        "There are actually multiple ways:\n",
        "\n",
        "*   Local upload from current machine.\n",
        "*   Go to the file menu and mount your Google Drive.\n",
        "*   Upload from Google Sheets.\n",
        "*   Straight download from a Uniform Resource Locator (URL).\n",
        "\n",
        "We will begin with loading from your local machine. First, download from the MyMason Blackboard site the .csv file `virginia.csv`.\n",
        "\n",
        "Now run the code cell below, select the data and import.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Loading data from a local machine\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "JaOiiDdDgxY3",
        "outputId": "f2753690-283f-4ac6-a6d9-e442d4445fa1"
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              "      Upload widget is only available when the cell has been executed in the\n",
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              "      <script>// Copyright 2017 Google LLC\n",
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              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
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              "  // Disable the input element since further picks are not allowed.\n",
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        {
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          "name": "stdout",
          "text": [
            "Saving virginia.csv to virginia.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we have this Comma Separated Value (.csv) file in our colab memory, we can import this data using pandas.\n",
        "\n",
        "We will import the package as normal, e.g., `import pandas`.\n",
        "\n",
        "To simplify the calling of this package, we can specify a shortened version of the name using the in-built Python command `as`. It is common to load `pandas` in short form as `pd`.\n",
        "\n",
        "Moreover, once loaded we can import the file using the `.read_csv()` function, and then passing the filepath or filename. As we have loaded this data already into our memory, calling via the filename is sufficient to find the data file.\n",
        "  "
      ],
      "metadata": {
        "id": "DE_lj6j0hYNU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Importing the pandas package\n",
        "import pandas as pd\n",
        "\n",
        "# Example: Importing the .csv file via `.read_csv()`\n",
        "df = pd.read_csv('virginia.csv')\n",
        "\n",
        "# Example: Viewing our dataframe\n",
        "df"
      ],
      "metadata": {
        "id": "FGhjYSbg620n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "outputId": "d7ca2d91-769b-4675-e5d5-6f98292ef76d"
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              "    GID_0         NAME_0     GID_1    NAME_1  NL_NAME_1         GID_2  \\\n",
              "0     USA  United States  USA.47_1  Virginia        NaN    USA.47.1_1   \n",
              "1     USA  United States  USA.47_1  Virginia        NaN    USA.47.2_1   \n",
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              "..    ...            ...       ...       ...        ...           ...   \n",
              "129   USA  United States  USA.47_1  Virginia        NaN  USA.47.130_1   \n",
              "130   USA  United States  USA.47_1  Virginia        NaN  USA.47.131_1   \n",
              "131   USA  United States  USA.47_1  Virginia        NaN  USA.47.132_1   \n",
              "132   USA  United States  USA.47_1  Virginia        NaN  USA.47.133_1   \n",
              "133   NaN            NaN       NaN       NaN        NaN           NaN   \n",
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              "         NAME_2  VARNAME_2  NL_NAME_2            TYPE_2         ENGTYPE_2  \\\n",
              "0      Accomack        NaN        NaN            County            County   \n",
              "1     Albemarle        NaN        NaN            County            County   \n",
              "2    Alexandria        NaN        NaN  Independent City  Independent City   \n",
              "3     Alleghany        NaN        NaN            County            County   \n",
              "4        Amelia        NaN        NaN            County            County   \n",
              "..          ...        ...        ...               ...               ...   \n",
              "129  Winchester        NaN        NaN  Independent City  Independent City   \n",
              "130        Wise        NaN        NaN            County            County   \n",
              "131       Wythe        NaN        NaN            County            County   \n",
              "132        York        NaN        NaN            County            County   \n",
              "133         NaN        NaN        NaN               NaN               NaN   \n",
              "\n",
              "     CC_2    HASC_2       area_m2  \n",
              "0     NaN  US.VA.AC  1.237202e+09  \n",
              "1     NaN  US.VA.AB  1.874287e+09  \n",
              "2     NaN  US.VA.AX  4.057266e+07  \n",
              "3     NaN  US.VA.AL  1.155584e+09  \n",
              "4     NaN  US.VA.AI  9.540803e+08  \n",
              "..    ...       ...           ...  \n",
              "129   NaN  US.VA.WH  2.348413e+07  \n",
              "130   NaN  US.VA.WI  1.046817e+09  \n",
              "131   NaN  US.VA.WY  1.202077e+09  \n",
              "132   NaN  US.VA.YO  2.821160e+08  \n",
              "133   NaN       NaN           NaN  \n",
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              "      <td>Virginia</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>US.VA.AC</td>\n",
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              "      <td>USA.47_1</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA.47.2_1</td>\n",
              "      <td>Albemarle</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>County</td>\n",
              "      <td>County</td>\n",
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              "      <td>US.VA.AB</td>\n",
              "      <td>1.874287e+09</td>\n",
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              "      <td>USA.47_1</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>Alexandria</td>\n",
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              "      <td>Independent City</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>US.VA.AX</td>\n",
              "      <td>4.057266e+07</td>\n",
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              "      <th>3</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA.47_1</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA.47.4_1</td>\n",
              "      <td>Alleghany</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>County</td>\n",
              "      <td>County</td>\n",
              "      <td>NaN</td>\n",
              "      <td>US.VA.AL</td>\n",
              "      <td>1.155584e+09</td>\n",
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              "      <th>4</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA.47_1</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA.47.5_1</td>\n",
              "      <td>Amelia</td>\n",
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              "      <td>County</td>\n",
              "      <td>County</td>\n",
              "      <td>NaN</td>\n",
              "      <td>US.VA.AI</td>\n",
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              "      <th>129</th>\n",
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              "      <td>United States</td>\n",
              "      <td>USA.47_1</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA.47.130_1</td>\n",
              "      <td>Winchester</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>Independent City</td>\n",
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              "      <td>Virginia</td>\n",
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              "      <td>Wise</td>\n",
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              "      <td>Virginia</td>\n",
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              "      <td>County</td>\n",
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              "      <td>US.VA.WY</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA.47_1</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA.47.133_1</td>\n",
              "      <td>York</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>County</td>\n",
              "      <td>County</td>\n",
              "      <td>NaN</td>\n",
              "      <td>US.VA.YO</td>\n",
              "      <td>2.821160e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\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",
              "      <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>134 rows × 14 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-96a5c08b-29d6-4902-b520-62c1aefc4d27')\"\n",
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              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
              "    }\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        document.querySelector('#df-96a5c08b-29d6-4902-b520-62c1aefc4d27 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-96a5c08b-29d6-4902-b520-62c1aefc4d27');\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",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-05904d26-37da-465e-b12c-7029366b1d31 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_8dc499a0-8876-47cc-988e-da504adc15bb\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_8dc499a0-8876-47cc-988e-da504adc15bb button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 134,\n  \"fields\": [\n    {\n      \"column\": \"GID_0\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"USA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NAME_0\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"United States\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GID_1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"USA.47_1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NAME_1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Virginia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NL_NAME_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GID_2\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 133,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NAME_2\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 133,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"VARNAME_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NL_NAME_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TYPE_2\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ENGTYPE_2\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CC_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HASC_2\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 130,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area_m2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 546761465.2255269,\n        \"min\": 5121049.0,\n        \"max\": 2135881041.0,\n        \"num_unique_values\": 130,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Task\n",
        "\n",
        "* Save your .csv file as an excel file (e.g., .xlsx) (Hint: read the documentation).\n",
        "* Work out how to import this file using `pandas` (e.g., using `.read_excel()`).\n",
        "\n"
      ],
      "metadata": {
        "id": "-kNlO6QHjj79"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Insert your attempt here\n"
      ],
      "metadata": {
        "id": "lVhF3dhS0V50"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Manipulating our dataframe\n",
        "\n",
        "Now we have our data loaded into the memory of our colab notebook, we can begin exploring.\n",
        "\n",
        "You can examine the top *n* rows of your dataframe using the `.head()` function."
      ],
      "metadata": {
        "id": "gyYAeSRZ63Nm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Inspecting the head of a dataframe (n=5 by default)\n",
        "# Note: Try specify the number of desired rows\n",
        "df.head()"
      ],
      "metadata": {
        "id": "O6FAJb-863hF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "outputId": "a31733e6-c81c-4b49-8503-0920f345da3f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  GID_0         NAME_0     GID_1    NAME_1  NL_NAME_1       GID_2      NAME_2  \\\n",
              "0   USA  United States  USA.47_1  Virginia        NaN  USA.47.1_1    Accomack   \n",
              "1   USA  United States  USA.47_1  Virginia        NaN  USA.47.2_1   Albemarle   \n",
              "2   USA  United States  USA.47_1  Virginia        NaN  USA.47.3_1  Alexandria   \n",
              "3   USA  United States  USA.47_1  Virginia        NaN  USA.47.4_1   Alleghany   \n",
              "4   USA  United States  USA.47_1  Virginia        NaN  USA.47.5_1      Amelia   \n",
              "\n",
              "   VARNAME_2  NL_NAME_2            TYPE_2         ENGTYPE_2  CC_2    HASC_2  \\\n",
              "0        NaN        NaN            County            County   NaN  US.VA.AC   \n",
              "1        NaN        NaN            County            County   NaN  US.VA.AB   \n",
              "2        NaN        NaN  Independent City  Independent City   NaN  US.VA.AX   \n",
              "3        NaN        NaN            County            County   NaN  US.VA.AL   \n",
              "4        NaN        NaN            County            County   NaN  US.VA.AI   \n",
              "\n",
              "        area_m2  \n",
              "0  1.237202e+09  \n",
              "1  1.874287e+09  \n",
              "2  4.057266e+07  \n",
              "3  1.155584e+09  \n",
              "4  9.540803e+08  "
            ],
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              "\n",
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              "    <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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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>GID_0</th>\n",
              "      <th>NAME_0</th>\n",
              "      <th>GID_1</th>\n",
              "      <th>NAME_1</th>\n",
              "      <th>NL_NAME_1</th>\n",
              "      <th>GID_2</th>\n",
              "      <th>NAME_2</th>\n",
              "      <th>VARNAME_2</th>\n",
              "      <th>NL_NAME_2</th>\n",
              "      <th>TYPE_2</th>\n",
              "      <th>ENGTYPE_2</th>\n",
              "      <th>CC_2</th>\n",
              "      <th>HASC_2</th>\n",
              "      <th>area_m2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA.47_1</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA.47.1_1</td>\n",
              "      <td>Accomack</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>County</td>\n",
              "      <td>County</td>\n",
              "      <td>NaN</td>\n",
              "      <td>US.VA.AC</td>\n",
              "      <td>1.237202e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA.47_1</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA.47.2_1</td>\n",
              "      <td>Albemarle</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>County</td>\n",
              "      <td>County</td>\n",
              "      <td>NaN</td>\n",
              "      <td>US.VA.AB</td>\n",
              "      <td>1.874287e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA.47_1</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA.47.3_1</td>\n",
              "      <td>Alexandria</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Independent City</td>\n",
              "      <td>Independent City</td>\n",
              "      <td>NaN</td>\n",
              "      <td>US.VA.AX</td>\n",
              "      <td>4.057266e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA.47_1</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA.47.4_1</td>\n",
              "      <td>Alleghany</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>County</td>\n",
              "      <td>County</td>\n",
              "      <td>NaN</td>\n",
              "      <td>US.VA.AL</td>\n",
              "      <td>1.155584e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA.47_1</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA.47.5_1</td>\n",
              "      <td>Amelia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>County</td>\n",
              "      <td>County</td>\n",
              "      <td>NaN</td>\n",
              "      <td>US.VA.AI</td>\n",
              "      <td>9.540803e+08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 134,\n  \"fields\": [\n    {\n      \"column\": \"GID_0\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"USA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NAME_0\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"United States\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GID_1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"USA.47_1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NAME_1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Virginia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NL_NAME_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GID_2\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 133,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NAME_2\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 133,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"VARNAME_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NL_NAME_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TYPE_2\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ENGTYPE_2\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CC_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HASC_2\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 130,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area_m2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 546761465.2255269,\n        \"min\": 5121049.0,\n        \"max\": 2135881041.0,\n        \"num_unique_values\": 130,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Equally, you can inspect the tail of your dataframe using the `.tail()` function."
      ],
      "metadata": {
        "id": "D5Pty7Wt63m0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Inspecting the tail of a dataframe (n=5 by default)\n",
        "# Note: Try specify the number of desired rows\n",
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "id": "aiKselJzk0dq",
        "outputId": "8eb67da0-1ef1-4ffd-b5c7-329257032e18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    GID_0         NAME_0     GID_1    NAME_1  NL_NAME_1         GID_2  \\\n",
              "129   USA  United States  USA.47_1  Virginia        NaN  USA.47.130_1   \n",
              "130   USA  United States  USA.47_1  Virginia        NaN  USA.47.131_1   \n",
              "131   USA  United States  USA.47_1  Virginia        NaN  USA.47.132_1   \n",
              "132   USA  United States  USA.47_1  Virginia        NaN  USA.47.133_1   \n",
              "133   NaN            NaN       NaN       NaN        NaN           NaN   \n",
              "\n",
              "         NAME_2  VARNAME_2  NL_NAME_2            TYPE_2         ENGTYPE_2  \\\n",
              "129  Winchester        NaN        NaN  Independent City  Independent City   \n",
              "130        Wise        NaN        NaN            County            County   \n",
              "131       Wythe        NaN        NaN            County            County   \n",
              "132        York        NaN        NaN            County            County   \n",
              "133         NaN        NaN        NaN               NaN               NaN   \n",
              "\n",
              "     CC_2    HASC_2       area_m2  \n",
              "129   NaN  US.VA.WH  2.348413e+07  \n",
              "130   NaN  US.VA.WI  1.046817e+09  \n",
              "131   NaN  US.VA.WY  1.202077e+09  \n",
              "132   NaN  US.VA.YO  2.821160e+08  \n",
              "133   NaN       NaN           NaN  "
            ],
            "text/html": [
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              "    <div>\n",
              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>GID_0</th>\n",
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              "      <th>area_m2</th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA.47_1</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA.47.130_1</td>\n",
              "      <td>Winchester</td>\n",
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              "      <td>Independent City</td>\n",
              "      <td>NaN</td>\n",
              "      <td>US.VA.WH</td>\n",
              "      <td>2.348413e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA.47_1</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA.47.131_1</td>\n",
              "      <td>Wise</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>County</td>\n",
              "      <td>County</td>\n",
              "      <td>NaN</td>\n",
              "      <td>US.VA.WI</td>\n",
              "      <td>1.046817e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA.47_1</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA.47.132_1</td>\n",
              "      <td>Wythe</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>County</td>\n",
              "      <td>County</td>\n",
              "      <td>NaN</td>\n",
              "      <td>US.VA.WY</td>\n",
              "      <td>1.202077e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA.47_1</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USA.47.133_1</td>\n",
              "      <td>York</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>County</td>\n",
              "      <td>County</td>\n",
              "      <td>NaN</td>\n",
              "      <td>US.VA.YO</td>\n",
              "      <td>2.821160e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\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",
              "      <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",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"GID_0\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"USA\"\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NAME_0\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"United States\"\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GID_1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"USA.47_1\"\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NAME_1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"Virginia\"\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NL_NAME_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": NaN,\n        \"min\": NaN,\n        \"max\": NaN,\n        \"samples\": [],\n        \"num_unique_values\": 0,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GID_2\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NAME_2\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"VARNAME_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": NaN,\n        \"min\": NaN,\n        \"max\": NaN,\n        \"samples\": [],\n        \"num_unique_values\": 0,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NL_NAME_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": NaN,\n        \"min\": NaN,\n        \"max\": NaN,\n        \"samples\": [],\n        \"num_unique_values\": 0,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TYPE_2\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ENGTYPE_2\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CC_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": NaN,\n        \"min\": NaN,\n        \"max\": NaN,\n        \"samples\": [],\n        \"num_unique_values\": 0,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HASC_2\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area_m2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 574338789.4021423,\n        \"min\": 23484130.0,\n        \"max\": 1202076507.0,\n        \"samples\": [],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Before we get started with summarizing the data, it is useful to understand the metadata associated with the dataframe.\n",
        "\n",
        "We can do this via the `.info()` function, which reports column names, non-null counts and the data type (Dtype)."
      ],
      "metadata": {
        "id": "dTacYlYElRcF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Associated metadata for our dataframe\n",
        "df.info()"
      ],
      "metadata": {
        "id": "MMcmYDxK63sR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b1eb03b-8861-45bc-e23d-c4e7e00ff8b7"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 134 entries, 0 to 133\n",
            "Data columns (total 14 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   GID_0      133 non-null    object \n",
            " 1   NAME_0     133 non-null    object \n",
            " 2   GID_1      133 non-null    object \n",
            " 3   NAME_1     133 non-null    object \n",
            " 4   NL_NAME_1  0 non-null      float64\n",
            " 5   GID_2      133 non-null    object \n",
            " 6   NAME_2     133 non-null    object \n",
            " 7   VARNAME_2  0 non-null      float64\n",
            " 8   NL_NAME_2  0 non-null      float64\n",
            " 9   TYPE_2     133 non-null    object \n",
            " 10  ENGTYPE_2  133 non-null    object \n",
            " 11  CC_2       0 non-null      float64\n",
            " 12  HASC_2     133 non-null    object \n",
            " 13  area_m2    130 non-null    float64\n",
            "dtypes: float64(5), object(9)\n",
            "memory usage: 14.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Moreover, we can find out how many rows and columns we have by utilizing the `.shape()` function."
      ],
      "metadata": {
        "id": "THbxx0E963xz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Obtaining the dataframe row and column count\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xKXkvmpkmGV6",
        "outputId": "7dab54ff-c147-4d57-ba8a-aa9065e8550e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(134, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Additionally, we can print the column dataframe names as follows, using the `.columns` function.\n",
        "\n",
        "This is handy when writing a normal `python` script, without the easy functionality of a notebook.   "
      ],
      "metadata": {
        "id": "oAqNtrMNmQt0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Viewing a summary of the dataframe column names\n",
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kGG6a0TAmU-a",
        "outputId": "27f9ec38-cfa3-4379-84bb-f560c8e59726"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['GID_0', 'NAME_0', 'GID_1', 'NAME_1', 'NL_NAME_1', 'GID_2', 'NAME_2',\n",
              "       'VARNAME_2', 'NL_NAME_2', 'TYPE_2', 'ENGTYPE_2', 'CC_2', 'HASC_2',\n",
              "       'area_m2'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Equally, we can view the dataframe index values using the `.index` function.\n",
        "\n",
        "The index is an essential piece of information in both a `pandas` series and dataframe, as it provides a label ID for each row. Thus, if we desire, we can uniquely identify and access individual row elements, values or subsets of the data."
      ],
      "metadata": {
        "id": "0MGkeaFummva"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Viewing a summary of the dataframe index\n",
        "df.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1K4G0rxnmi7J",
        "outputId": "4035b419-005e-4736-dd5a-8ef2d848fb13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=134, step=1)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To select a row, we can use the `.loc[]` function to index, and then pass our index value.\n",
        "\n",
        "We can do this for the zero index location as follows."
      ],
      "metadata": {
        "id": "jmfuEqpBnGKq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: We can select the first row in index location zero\n",
        "# Note: Try manually subset other rows\n",
        "df.loc[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "j_5zJVwXnJS5",
        "outputId": "645819e6-1bf9-4f58-a767-a31b9de90b53"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GID_0                  USA\n",
              "NAME_0       United States\n",
              "GID_1             USA.47_1\n",
              "NAME_1            Virginia\n",
              "NL_NAME_1              NaN\n",
              "GID_2           USA.47.1_1\n",
              "NAME_2            Accomack\n",
              "VARNAME_2              NaN\n",
              "NL_NAME_2              NaN\n",
              "TYPE_2              County\n",
              "ENGTYPE_2           County\n",
              "CC_2                   NaN\n",
              "HASC_2            US.VA.AC\n",
              "area_m2       1237202429.0\n",
              "Name: 0, dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "      <th>GID_0</th>\n",
              "      <td>USA</td>\n",
              "    </tr>\n",
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              "      <td>United States</td>\n",
              "    </tr>\n",
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              "      <th>GID_1</th>\n",
              "      <td>USA.47_1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NAME_1</th>\n",
              "      <td>Virginia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NL_NAME_1</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GID_2</th>\n",
              "      <td>USA.47.1_1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NAME_2</th>\n",
              "      <td>Accomack</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VARNAME_2</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NL_NAME_2</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TYPE_2</th>\n",
              "      <td>County</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ENGTYPE_2</th>\n",
              "      <td>County</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CC_2</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HASC_2</th>\n",
              "      <td>US.VA.AC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_m2</th>\n",
              "      <td>1237202429.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Often we might have to deal with missing data.\n",
        "\n",
        "There are multiple ways, but here a few important ones are outlined\n",
        "\n",
        "We can query a dataframe to check if the values are `NaN` or not using the `.isnull()` function.\n",
        "\n",
        "This returns a True/False dataframe indicating missing values.\n",
        "\n",
        "Note: `NaN` stands for \"Not a Number\" and is indicative of missing or undefined data (e.g., null values).\n"
      ],
      "metadata": {
        "id": "nqdUpV41nw4n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Checking a dataframe for missing data\n",
        "df.isnull()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "ov8kCtK8nxdX",
        "outputId": "874654d2-fcff-4e4d-d4ee-8f22ca72286b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
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              "     GID_0  NAME_0  GID_1  NAME_1  NL_NAME_1  GID_2  NAME_2  VARNAME_2  \\\n",
              "0    False   False  False   False       True  False   False       True   \n",
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              "..         ...     ...        ...   ...     ...      ...  \n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 134,\n  \"fields\": [\n    {\n      \"column\": \"GID_0\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NAME_0\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GID_1\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NAME_1\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NL_NAME_1\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GID_2\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NAME_2\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"VARNAME_2\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NL_NAME_2\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TYPE_2\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ENGTYPE_2\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CC_2\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HASC_2\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area_m2\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can also just drop all rows with missing data via the `.dropna()` function.\n",
        "\n",
        "As we have entire columns of missing data in our dataframe, this will mean all rows are dropped."
      ],
      "metadata": {
        "id": "8qqo55xinyQK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Dropping rows with missing data (e.g., NaN)\n",
        "df.dropna()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "RPoW_qPyoadE",
        "outputId": "f7bf9a32-e3a3-41ff-8f5a-97195a77defa"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [GID_0, NAME_0, GID_1, NAME_1, NL_NAME_1, GID_2, NAME_2, VARNAME_2, NL_NAME_2, TYPE_2, ENGTYPE_2, CC_2, HASC_2, area_m2]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-941f5758-b4a8-4eee-b048-673fc8b60d85\" class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GID_0</th>\n",
              "      <th>NAME_0</th>\n",
              "      <th>GID_1</th>\n",
              "      <th>NAME_1</th>\n",
              "      <th>NL_NAME_1</th>\n",
              "      <th>GID_2</th>\n",
              "      <th>NAME_2</th>\n",
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              "      <th>NL_NAME_2</th>\n",
              "      <th>TYPE_2</th>\n",
              "      <th>ENGTYPE_2</th>\n",
              "      <th>CC_2</th>\n",
              "      <th>HASC_2</th>\n",
              "      <th>area_m2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-941f5758-b4a8-4eee-b048-673fc8b60d85')\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-941f5758-b4a8-4eee-b048-673fc8b60d85 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-941f5758-b4a8-4eee-b048-673fc8b60d85');\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",
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              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "Out of range float values are not JSON compliant: nan"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "If you want to avoid this, we could take a subset of columns which we know are generally complete, using a double set of index parentheses (`[[]]`) containing the column names we want to keep.\n",
        "\n",
        "In `pandas`, we can subset columns of interest like so."
      ],
      "metadata": {
        "id": "Q8fBJ6LcpIQK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Subsetting columns of interest\n",
        "df[['NAME_0', 'NAME_2', 'area_m2']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "E7T9-ceBpI-K",
        "outputId": "d0db5018-0b92-4620-a1f0-4310ff951749"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            NAME_0      NAME_2       area_m2\n",
              "0    United States    Accomack  1.237202e+09\n",
              "1    United States   Albemarle  1.874287e+09\n",
              "2    United States  Alexandria  4.057266e+07\n",
              "3    United States   Alleghany  1.155584e+09\n",
              "4    United States      Amelia  9.540803e+08\n",
              "..             ...         ...           ...\n",
              "129  United States  Winchester  2.348413e+07\n",
              "130  United States        Wise  1.046817e+09\n",
              "131  United States       Wythe  1.202077e+09\n",
              "132  United States        York  2.821160e+08\n",
              "133            NaN         NaN           NaN\n",
              "\n",
              "[134 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>United States</td>\n",
              "      <td>Accomack</td>\n",
              "      <td>1.237202e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>United States</td>\n",
              "      <td>Albemarle</td>\n",
              "      <td>1.874287e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>United States</td>\n",
              "      <td>Alexandria</td>\n",
              "      <td>4.057266e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>United States</td>\n",
              "      <td>Alleghany</td>\n",
              "      <td>1.155584e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>United States</td>\n",
              "      <td>Amelia</td>\n",
              "      <td>9.540803e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>United States</td>\n",
              "      <td>Winchester</td>\n",
              "      <td>2.348413e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>United States</td>\n",
              "      <td>Wise</td>\n",
              "      <td>1.046817e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>United States</td>\n",
              "      <td>Wythe</td>\n",
              "      <td>1.202077e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>United States</td>\n",
              "      <td>York</td>\n",
              "      <td>2.821160e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>134 rows × 3 columns</p>\n",
              "</div>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-28304dce-e0c5-4bc4-8070-5eba0bd25b0d button.colab-df-convert');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-28304dce-e0c5-4bc4-8070-5eba0bd25b0d');\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",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    height: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "\n",
              "  @keyframes spin {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[['NAME_0', 'NAME_2', 'area_m2']]\",\n  \"rows\": 134,\n  \"fields\": [\n    {\n      \"column\": \"NAME_0\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"United States\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NAME_2\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 133,\n        \"samples\": [\n          \"Goochland\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area_m2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 546761465.2255269,\n        \"min\": 5121049.0,\n        \"max\": 2135881041.0,\n        \"num_unique_values\": 130,\n        \"samples\": [\n          138574869.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we can retry our `.dropna()` function, like so.\n",
        "\n",
        "We will reallocate the columns we want to subset to a new dataframe called `subset`."
      ],
      "metadata": {
        "id": "hLLH2HVOpvfJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Subset our dataframe and drop rows with missing data\n",
        "\n",
        "# Note: We subset our data and allocate to new dataframe variable name\n",
        "subset = df[['NAME_0', 'NAME_2', 'area_m2']]\n",
        "\n",
        "# Drop rows with missing data\n",
        "subset = subset.dropna()\n",
        "print(\"Original df length is {}\".format(len(df)))\n",
        "print(\"New df length is {}\".format(len(subset)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VkQ0RJWUpwOJ",
        "outputId": "fcbd185b-b5ac-4ebf-c1ce-f955fd27c4d7"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original df length is 134\n",
            "New df length is 130\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Task\n",
        "\n",
        "Subset the GID_0, NAME_0, GID_1, NAME_1, GID_2 and NAME_2 columns to a new dataframe called `subset2`.\n",
        "\n",
        "Drop rows with missing data.\n",
        "\n",
        "Check the length of the original and final dataframes to inspect the differences."
      ],
      "metadata": {
        "id": "EHzWGbQEzZ3n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Insert your attempt here\n"
      ],
      "metadata": {
        "id": "Y7pLaKqu0SDV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "You could choose to fill missing data points with values of your choice, via the `.fillna()` function.\n",
        "\n",
        "Just be careful using this, **you do not want to arbitrarily insert values of your own choice into a statistical distribution!**"
      ],
      "metadata": {
        "id": "EvYnXFsTqwj3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Filling missing values with a desired value\n",
        "df_filled = df.fillna(0)\n",
        "df_filled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "j5xSa_sMqxK3",
        "outputId": "b69ee271-82b8-4243-956d-5806e7c651ff"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    GID_0         NAME_0     GID_1    NAME_1  NL_NAME_1         GID_2  \\\n",
              "0     USA  United States  USA.47_1  Virginia        0.0    USA.47.1_1   \n",
              "1     USA  United States  USA.47_1  Virginia        0.0    USA.47.2_1   \n",
              "2     USA  United States  USA.47_1  Virginia        0.0    USA.47.3_1   \n",
              "3     USA  United States  USA.47_1  Virginia        0.0    USA.47.4_1   \n",
              "4     USA  United States  USA.47_1  Virginia        0.0    USA.47.5_1   \n",
              "..    ...            ...       ...       ...        ...           ...   \n",
              "129   USA  United States  USA.47_1  Virginia        0.0  USA.47.130_1   \n",
              "130   USA  United States  USA.47_1  Virginia        0.0  USA.47.131_1   \n",
              "131   USA  United States  USA.47_1  Virginia        0.0  USA.47.132_1   \n",
              "132   USA  United States  USA.47_1  Virginia        0.0  USA.47.133_1   \n",
              "133     0              0         0         0        0.0             0   \n",
              "\n",
              "         NAME_2  VARNAME_2  NL_NAME_2            TYPE_2         ENGTYPE_2  \\\n",
              "0      Accomack        0.0        0.0            County            County   \n",
              "1     Albemarle        0.0        0.0            County            County   \n",
              "2    Alexandria        0.0        0.0  Independent City  Independent City   \n",
              "3     Alleghany        0.0        0.0            County            County   \n",
              "4        Amelia        0.0        0.0            County            County   \n",
              "..          ...        ...        ...               ...               ...   \n",
              "129  Winchester        0.0        0.0  Independent City  Independent City   \n",
              "130        Wise        0.0        0.0            County            County   \n",
              "131       Wythe        0.0        0.0            County            County   \n",
              "132        York        0.0        0.0            County            County   \n",
              "133           0        0.0        0.0                 0                 0   \n",
              "\n",
              "     CC_2    HASC_2       area_m2  \n",
              "0     0.0  US.VA.AC  1.237202e+09  \n",
              "1     0.0  US.VA.AB  1.874287e+09  \n",
              "2     0.0  US.VA.AX  4.057266e+07  \n",
              "3     0.0  US.VA.AL  1.155584e+09  \n",
              "4     0.0  US.VA.AI  9.540803e+08  \n",
              "..    ...       ...           ...  \n",
              "129   0.0  US.VA.WH  2.348413e+07  \n",
              "130   0.0  US.VA.WI  1.046817e+09  \n",
              "131   0.0  US.VA.WY  1.202077e+09  \n",
              "132   0.0  US.VA.YO  2.821160e+08  \n",
              "133   0.0         0  0.000000e+00  \n",
              "\n",
              "[134 rows x 14 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-834d2ffc-f77c-49eb-96fc-9626f13e9ea4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GID_0</th>\n",
              "      <th>NAME_0</th>\n",
              "      <th>GID_1</th>\n",
              "      <th>NAME_1</th>\n",
              "      <th>NL_NAME_1</th>\n",
              "      <th>GID_2</th>\n",
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              "      <th>VARNAME_2</th>\n",
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              "      <th>TYPE_2</th>\n",
              "      <th>ENGTYPE_2</th>\n",
              "      <th>CC_2</th>\n",
              "      <th>HASC_2</th>\n",
              "      <th>area_m2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA.47_1</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>0.0</td>\n",
              "      <td>USA.47.1_1</td>\n",
              "      <td>Accomack</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>County</td>\n",
              "      <td>County</td>\n",
              "      <td>0.0</td>\n",
              "      <td>US.VA.AC</td>\n",
              "      <td>1.237202e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA.47_1</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>0.0</td>\n",
              "      <td>USA.47.2_1</td>\n",
              "      <td>Albemarle</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>County</td>\n",
              "      <td>County</td>\n",
              "      <td>0.0</td>\n",
              "      <td>US.VA.AB</td>\n",
              "      <td>1.874287e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA.47_1</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>0.0</td>\n",
              "      <td>USA.47.3_1</td>\n",
              "      <td>Alexandria</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Independent City</td>\n",
              "      <td>Independent City</td>\n",
              "      <td>0.0</td>\n",
              "      <td>US.VA.AX</td>\n",
              "      <td>4.057266e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA.47_1</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>0.0</td>\n",
              "      <td>USA.47.4_1</td>\n",
              "      <td>Alleghany</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>County</td>\n",
              "      <td>County</td>\n",
              "      <td>0.0</td>\n",
              "      <td>US.VA.AL</td>\n",
              "      <td>1.155584e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA.47_1</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>0.0</td>\n",
              "      <td>USA.47.5_1</td>\n",
              "      <td>Amelia</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>County</td>\n",
              "      <td>County</td>\n",
              "      <td>0.0</td>\n",
              "      <td>US.VA.AI</td>\n",
              "      <td>9.540803e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA.47_1</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>0.0</td>\n",
              "      <td>USA.47.130_1</td>\n",
              "      <td>Winchester</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Independent City</td>\n",
              "      <td>Independent City</td>\n",
              "      <td>0.0</td>\n",
              "      <td>US.VA.WH</td>\n",
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              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA.47_1</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>0.0</td>\n",
              "      <td>USA.47.131_1</td>\n",
              "      <td>Wise</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>County</td>\n",
              "      <td>County</td>\n",
              "      <td>0.0</td>\n",
              "      <td>US.VA.WI</td>\n",
              "      <td>1.046817e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>USA</td>\n",
              "      <td>United States</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>USA.47.132_1</td>\n",
              "      <td>Wythe</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>County</td>\n",
              "      <td>County</td>\n",
              "      <td>0.0</td>\n",
              "      <td>US.VA.WY</td>\n",
              "      <td>1.202077e+09</td>\n",
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              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>USA</td>\n",
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              "      <td>Virginia</td>\n",
              "      <td>0.0</td>\n",
              "      <td>USA.47.133_1</td>\n",
              "      <td>York</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>County</td>\n",
              "      <td>County</td>\n",
              "      <td>0.0</td>\n",
              "      <td>US.VA.YO</td>\n",
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              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>134 rows × 14 columns</p>\n",
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              "        const element = document.querySelector('#df-834d2ffc-f77c-49eb-96fc-9626f13e9ea4');\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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              "type": "dataframe",
              "variable_name": "df_filled",
              "summary": "{\n  \"name\": \"df_filled\",\n  \"rows\": 134,\n  \"fields\": [\n    {\n      \"column\": \"GID_0\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          \"USA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NAME_0\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          \"United States\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GID_1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          \"USA.47_1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NAME_1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          \"Virginia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NL_NAME_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GID_2\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 134,\n        \"samples\": [\n          \"USA.47.128_1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NAME_2\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 134,\n        \"samples\": [\n          \"Westmoreland\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"VARNAME_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NL_NAME_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TYPE_2\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Independent City\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ENGTYPE_2\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"County\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CC_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HASC_2\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 131,\n        \"samples\": [\n          \"US.VA.HV\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area_m2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 553202707.3977337,\n        \"min\": 0.0,\n        \"max\": 2135881041.0,\n        \"num_unique_values\": 131,\n        \"samples\": [\n          2135881041.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can obtain summary statistics for a specific column by indexing with the column name, as follows."
      ],
      "metadata": {
        "id": "dRVjhH-w637T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Indexing by column name\n",
        "df['area_m2'].sum()"
      ],
      "metadata": {
        "id": "SeDA3fXQ63_k",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aedbec3a-cc6d-4a39-86be-3b0436340e5f"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "96496076219.0"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have a variety of statistical functions at our finger tips."
      ],
      "metadata": {
        "id": "OibADQSP64ER"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Summarizing a column\n",
        "print(df['area_m2'].mean())\n",
        "print(df['area_m2'].median())\n",
        "print(df['area_m2'].min())\n",
        "print(df['area_m2'].max())"
      ],
      "metadata": {
        "id": "F067h77j7dAp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32537910-32e3-4847-fdd4-414598385f4e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "742277509.3769231\n",
            "770881080.0\n",
            "5121049.0\n",
            "2135881041.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can also summarize the unique values."
      ],
      "metadata": {
        "id": "uemLq0f04eIU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Unique values for Virginia area names\n",
        "df['NAME_2'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QJH0akhT4dug",
        "outputId": "1e4b9af2-a33d-4473-84a6-0bdb99aca6de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Accomack', 'Albemarle', 'Alexandria', 'Alleghany', 'Amelia',\n",
              "       'Amherst', 'Appomattox', 'Arlington', 'Augusta', 'Bath', 'Bedford',\n",
              "       'Bedford City', 'Bland', 'Botetourt', 'Bristol', 'Brunswick',\n",
              "       'Buchanan', 'Buckingham', 'Buena Vista', 'Campbell', 'Caroline',\n",
              "       'Carroll', 'Charles City', 'Charlotte', 'Charlottesville',\n",
              "       'Chesapeake', 'Chesterfield', 'Clarke', 'Clifton Forge City',\n",
              "       'Colonial Heights', 'Covington', 'Craig', 'Culpeper', 'Cumberland',\n",
              "       'Danville', 'Dickenson', 'Dinwiddie', 'Emporia', 'Essex',\n",
              "       'Fairfax', 'Fairfax City', 'Falls Church', 'Fauquier', 'Floyd',\n",
              "       'Fluvanna', 'Franklin', 'Frederick', 'Fredericksburg', 'Galax',\n",
              "       'Giles', 'Gloucester', 'Goochland', 'Grayson', 'Greene',\n",
              "       'Greensville', 'Halifax', 'Hampton', 'Hanover', 'Harrisonburg',\n",
              "       'Henrico', 'Henry', 'Highland', 'Hopewell', 'Isle of Wight',\n",
              "       'James City', 'King and Queen', 'King George', 'King William',\n",
              "       'Lancaster', 'Lee', 'Lexington', 'Loudoun', 'Louisa', 'Lunenburg',\n",
              "       'Lynchburg', 'Madison', 'Manassas', 'Manassas Park',\n",
              "       'Martinsville', 'Mathews', 'Mecklenburg', 'Middlesex',\n",
              "       'Montgomery', 'Nelson', 'New Kent', 'Newport News', 'Norfolk',\n",
              "       'Northampton', 'Northumberland', 'Norton', 'Nottoway', 'Orange',\n",
              "       'Page', 'Patrick', 'Petersburg', 'Pittsylvania', 'Poquoson',\n",
              "       'Portsmouth', 'Powhatan', 'Prince Edward', 'Prince George',\n",
              "       'Prince William', 'Pulaski', 'Radford', 'Rappahannock', 'Richmond',\n",
              "       'Roanoke', 'Roanoke City', 'Rockbridge', 'Rockingham', 'Russell',\n",
              "       'Salem', 'Scott', 'Shenandoah', 'Smyth', 'Southampton',\n",
              "       'Spotsylvania', 'Stafford', 'Staunton', 'Suffolk', 'Surry',\n",
              "       'Sussex', 'Tazewell', 'Virginia Beach', 'Warren', 'Washington',\n",
              "       'Waynesboro', 'Westmoreland', 'Williamsburg', 'Winchester', 'Wise',\n",
              "       'Wythe', 'York', nan], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Unique values for Virginia area names\n",
        "df['GID_0'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4P_KgsiO44wh",
        "outputId": "d91e44af-eafa-4939-9f73-ce33452ee20a"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['USA', nan], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importantly, we can group on certain columns and summarize, by first using the `.groupby()` function, followed by our statistical function name, such as `.mean()`."
      ],
      "metadata": {
        "id": "2ZrW3zHx7dWS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Groupby and summarize for the state of Virginia\n",
        "df = df[['NAME_1','area_m2']]\n",
        "df.groupby('NAME_1').mean()"
      ],
      "metadata": {
        "id": "IWMRBfVL7xx7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "41266764-781a-428f-b288-b0094c7969d9"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               area_m2\n",
              "NAME_1                \n",
              "Virginia  7.422775e+08"
            ],
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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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-a58b3e99-3fac-4ab8-a7e4-ab3077d0018b 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-a58b3e99-3fac-4ab8-a7e4-ab3077d0018b');\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",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"NAME_1\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Virginia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area_m2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 742277509.3769231,\n        \"max\": 742277509.3769231,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          742277509.3769231\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Groupby and summarize for adminstrative type\n",
        "df = pd.read_csv('virginia.csv')\n",
        "df = df[['TYPE_2','area_m2']]\n",
        "df.groupby('TYPE_2').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "bxIZNE-tx1ch",
        "outputId": "15298c63-d504-4bc0-b985-88e3a3ac7825"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       area_m2\n",
              "TYPE_2                        \n",
              "City              1.903495e+07\n",
              "County            1.005642e+09\n",
              "Independent City  1.336368e+08"
            ],
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              "\n",
              "  <div id=\"df-7c88ebf6-9468-4fd0-82b4-2889c28602d3\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>area_m2</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TYPE_2</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
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              "    <tr>\n",
              "      <th>City</th>\n",
              "      <td>1.903495e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>County</th>\n",
              "      <td>1.005642e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Independent City</th>\n",
              "      <td>1.336368e+08</td>\n",
              "    </tr>\n",
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              "      display:flex;\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7c88ebf6-9468-4fd0-82b4-2889c28602d3 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-7c88ebf6-9468-4fd0-82b4-2889c28602d3');\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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-60c33f5b-b24b-42d6-929b-46aaa1c25157')\"\n",
              "            title=\"Suggest charts\"\n",
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              "\n",
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              "  </button>\n",
              "\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-60c33f5b-b24b-42d6-929b-46aaa1c25157 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"TYPE_2\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"City\",\n          \"County\",\n          \"Independent City\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area_m2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 539586437.9839607,\n        \"min\": 19034949.5,\n        \"max\": 1005642261.9230769,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          19034949.5,\n          1005642261.9230769,\n          133636769.86486487\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It is possible for us to create new variables, we just need to specify the dataframe and new column name on the left hand side."
      ],
      "metadata": {
        "id": "iHl-ykft0mKW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Creating a new variable\n",
        "df['my_new_variable'] = 'my_new_value'\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "nsATd4AR0nom",
        "outputId": "53374a4f-7923-42ea-8217-8fbbca17aa84"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               TYPE_2       area_m2 my_new_variable\n",
              "0              County  1.237202e+09    my_new_value\n",
              "1              County  1.874287e+09    my_new_value\n",
              "2    Independent City  4.057266e+07    my_new_value\n",
              "3              County  1.155584e+09    my_new_value\n",
              "4              County  9.540803e+08    my_new_value\n",
              "..                ...           ...             ...\n",
              "129  Independent City  2.348413e+07    my_new_value\n",
              "130            County  1.046817e+09    my_new_value\n",
              "131            County  1.202077e+09    my_new_value\n",
              "132            County  2.821160e+08    my_new_value\n",
              "133               NaN           NaN    my_new_value\n",
              "\n",
              "[134 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4301b5dd-668f-4ad4-9c4f-c8418eea7310\" 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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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>TYPE_2</th>\n",
              "      <th>area_m2</th>\n",
              "      <th>my_new_variable</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>1.237202e+09</td>\n",
              "      <td>my_new_value</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>County</td>\n",
              "      <td>1.874287e+09</td>\n",
              "      <td>my_new_value</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Independent City</td>\n",
              "      <td>4.057266e+07</td>\n",
              "      <td>my_new_value</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>County</td>\n",
              "      <td>1.155584e+09</td>\n",
              "      <td>my_new_value</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>County</td>\n",
              "      <td>9.540803e+08</td>\n",
              "      <td>my_new_value</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>Independent City</td>\n",
              "      <td>2.348413e+07</td>\n",
              "      <td>my_new_value</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>County</td>\n",
              "      <td>1.046817e+09</td>\n",
              "      <td>my_new_value</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>County</td>\n",
              "      <td>1.202077e+09</td>\n",
              "      <td>my_new_value</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>County</td>\n",
              "      <td>2.821160e+08</td>\n",
              "      <td>my_new_value</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>my_new_value</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>134 rows × 3 columns</p>\n",
              "</div>\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-4301b5dd-668f-4ad4-9c4f-c8418eea7310 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-4301b5dd-668f-4ad4-9c4f-c8418eea7310');\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",
              "\n",
              "\n",
              "<div id=\"df-06af4c9a-f168-4ae5-aade-33c8a2455d10\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-06af4c9a-f168-4ae5-aade-33c8a2455d10')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-06af4c9a-f168-4ae5-aade-33c8a2455d10 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_8386cc6e-ebfa-4ddf-a4d7-06d85413fee4\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_8386cc6e-ebfa-4ddf-a4d7-06d85413fee4 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 134,\n  \"fields\": [\n    {\n      \"column\": \"TYPE_2\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"County\",\n          \"Independent City\",\n          \"City\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area_m2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 546761465.2255269,\n        \"min\": 5121049.0,\n        \"max\": 2135881041.0,\n        \"num_unique_values\": 130,\n        \"samples\": [\n          138574869.0,\n          5121049.0,\n          1374022337.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"my_new_variable\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"my_new_value\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Task\n",
        "\n",
        "Can you create a new variable, converting the area in square meters into square kilometers?\n"
      ],
      "metadata": {
        "id": "Qwq1ykVv08Hx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Insert your attempt here\n"
      ],
      "metadata": {
        "id": "suB2JWk708uT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now try find the mean, median, minimum and maximum values for the area based on the TYPE_2 column (in square kilometers).\n"
      ],
      "metadata": {
        "id": "gfb-kKZN1LK2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Insert your attempt here\n"
      ],
      "metadata": {
        "id": "x4ZqjMWj1M60"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Subset the GID_0, NAME_0, GID_1, NAME_1, GID_2 and NAME_2 columns to a new dataframe called subset2.\n",
        "\n",
        "Now obtain the unique rows. You can use Google/StackOverflow."
      ],
      "metadata": {
        "id": "7063nVnN5kTj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Insert your attempt here\n"
      ],
      "metadata": {
        "id": "zGu_UCMR5k-T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exporting data\n",
        "\n",
        "One of the most handy functions within pandas is being able to quickly export your data to a desired format.\n",
        "\n",
        "For example, we can use the `.to_csv()` function to export to a .csv file.\n",
        "\n",
        "Once you have run the cell below, look in the colab file browser for your file, and then download to your local machine to inspect.   \n"
      ],
      "metadata": {
        "id": "CiW-DZJIyVHW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Exporting dataframe data to a .csv\n",
        "df = pd.read_csv('virginia.csv')\n",
        "output = df[['NAME_0', 'NAME_2', 'area_m2']]\n",
        "output.to_csv('output.csv', index=False)"
      ],
      "metadata": {
        "id": "itktGNEaySgE"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Task\n",
        "\n",
        "* Export the original full dataframe to an excel .xlsx format.\n",
        "* Subset the original dataframe on the GID_2 and area columns. Export to a .csv file.\n",
        "* Summarize the area dataframe on the TYPE_2 column, and export as a .xlsx file.  \n"
      ],
      "metadata": {
        "id": "ojNe9aLhymAX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Insert your attempt here\n"
      ],
      "metadata": {
        "id": "HVIivwAc0Xc2"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}