Python_Basics_14(Pandas).ipynb

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            "      Gender     Height      Weight\n",
            "0       Male  73.847017  241.893563\n",
            "1       Male  68.781904  162.310473\n",
            "2       Male  74.110105  212.740856\n",
            "3       Male  71.730978  220.042470\n",
            "4       Male  69.881796  206.349801\n",
            "...      ...        ...         ...\n",
            "9995  Female  66.172652  136.777454\n",
            "9996  Female  67.067155  170.867906\n",
            "9997  Female  63.867992  128.475319\n",
            "9998  Female  69.034243  163.852461\n",
            "9999  Female  61.944246  113.649103\n",
            "\n",
            "[10000 rows x 3 columns]\n"
          ]
        }
      ],
      "source": [
        "#Pandas\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "data_set = pd.read_csv('weight-height.csv')\n",
        "print(data_set)\n"
      ]
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        "data_set.describe()"
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              "             Height        Weight\n",
              "count  10000.000000  10000.000000\n",
              "mean      66.367560    161.440357\n",
              "std        3.847528     32.108439\n",
              "min       54.263133     64.700127\n",
              "25%       63.505620    135.818051\n",
              "50%       66.318070    161.212928\n",
              "75%       69.174262    187.169525\n",
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              "Index(['Gender', 'Height', 'Weight'], dtype='object')"
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              "  Gender     Height      Weight\n",
              "0   Male  73.847017  241.893563\n",
              "1   Male  68.781904  162.310473\n",
              "2   Male  74.110105  212.740856\n",
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              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5d84bd0b-0724-4487-87c0-fcc389908dc6 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-5d84bd0b-0724-4487-87c0-fcc389908dc6');\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-71e05007-ee80-464f-8778-9786f17ab37c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-71e05007-ee80-464f-8778-9786f17ab37c')\"\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-71e05007-ee80-464f-8778-9786f17ab37c 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",
              "variable_name": "data_set",
              "summary": "{\n  \"name\": \"data_set\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.8475281207732293,\n        \"min\": 54.2631333250971,\n        \"max\": 78.9987423463896,\n        \"num_unique_values\": 10000,\n        \"samples\": [\n          68.6872321923365,\n          68.8293337723017\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.108439006519596,\n        \"min\": 64.700126712753,\n        \"max\": 269.989698505106,\n        \"num_unique_values\": 10000,\n        \"samples\": [\n          173.115812588004,\n          195.162042097346\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_set.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "jRDDbJjg1ZCs",
        "outputId": "3bfbf350-bd34-4dd3-f6b6-43664b469fe4"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Gender     Height      Weight\n",
              "9995  Female  66.172652  136.777454\n",
              "9996  Female  67.067155  170.867906\n",
              "9997  Female  63.867992  128.475319\n",
              "9998  Female  69.034243  163.852461\n",
              "9999  Female  61.944246  113.649103"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b45f16e4-2726-4739-ba5d-efc216863197\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>Female</td>\n",
              "      <td>66.172652</td>\n",
              "      <td>136.777454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>Female</td>\n",
              "      <td>67.067155</td>\n",
              "      <td>170.867906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>Female</td>\n",
              "      <td>63.867992</td>\n",
              "      <td>128.475319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>Female</td>\n",
              "      <td>69.034243</td>\n",
              "      <td>163.852461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>Female</td>\n",
              "      <td>61.944246</td>\n",
              "      <td>113.649103</td>\n",
              "    </tr>\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-b45f16e4-2726-4739-ba5d-efc216863197')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-b45f16e4-2726-4739-ba5d-efc216863197 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-b45f16e4-2726-4739-ba5d-efc216863197');\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-172053ef-4be8-4336-9599-4997288431bf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-172053ef-4be8-4336-9599-4997288431bf')\"\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-172053ef-4be8-4336-9599-4997288431bf 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\": \"data_set\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.767553033101086,\n        \"min\": 61.9442458795172,\n        \"max\": 69.0342431307346,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          67.067154649054\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24.094746806287993,\n        \"min\": 113.649102675312,\n        \"max\": 170.867905890713,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          170.867905890713\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_set.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OJpk70ab1bUb",
        "outputId": "f1246bef-bb3a-4ce9-a4aa-5a418d94fc5f"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Accessing particular column\n",
        "\n",
        "height = pd.Series(data_set['Height'])\n",
        "print(height)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QrzicbUn1gg7",
        "outputId": "1d14846f-1a9a-45f3-ff58-176ddcfd311a"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0       73.847017\n",
            "1       68.781904\n",
            "2       74.110105\n",
            "3       71.730978\n",
            "4       69.881796\n",
            "          ...    \n",
            "9995    66.172652\n",
            "9996    67.067155\n",
            "9997    63.867992\n",
            "9998    69.034243\n",
            "9999    61.944246\n",
            "Name: Height, Length: 10000, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#or\n",
        "\n",
        "data_set.Height"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "qGt3A1Lg2OSE",
        "outputId": "bbe8ef9f-d9c7-49a2-e91e-bd1498cb9ee6"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       73.847017\n",
              "1       68.781904\n",
              "2       74.110105\n",
              "3       71.730978\n",
              "4       69.881796\n",
              "          ...    \n",
              "9995    66.172652\n",
              "9996    67.067155\n",
              "9997    63.867992\n",
              "9998    69.034243\n",
              "9999    61.944246\n",
              "Name: Height, Length: 10000, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Height</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>73.847017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>68.781904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>74.110105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>71.730978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>69.881796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>66.172652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>67.067155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>63.867992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>69.034243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>61.944246</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#or\n",
        "\n",
        "weight = data_set['Weight']\n",
        "print(weight)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8rRPXjU82fUT",
        "outputId": "661c3f53-96de-4851-dc00-a619a4d4524d"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0       241.893563\n",
            "1       162.310473\n",
            "2       212.740856\n",
            "3       220.042470\n",
            "4       206.349801\n",
            "           ...    \n",
            "9995    136.777454\n",
            "9996    170.867906\n",
            "9997    128.475319\n",
            "9998    163.852461\n",
            "9999    113.649103\n",
            "Name: Weight, Length: 10000, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(height) ==  len(weight))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6q6KA2ne2ku8",
        "outputId": "46e9a0bb-18ee-4ba3-9f0e-8966e83d800f"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_set.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7EO4dq6j3HHb",
        "outputId": "7099d96d-bade-42b0-ec49-16f8de41fd32"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 3 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Gender  10000 non-null  object \n",
            " 1   Height  10000 non-null  float64\n",
            " 2   Weight  10000 non-null  float64\n",
            "dtypes: float64(2), object(1)\n",
            "memory usage: 234.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "height.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "u0ZC4qQK3J3D",
        "outputId": "ddb58f12-ec19-4d37-cf1c-4a9656e897d5"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    10000.000000\n",
              "mean        66.367560\n",
              "std          3.847528\n",
              "min         54.263133\n",
              "25%         63.505620\n",
              "50%         66.318070\n",
              "75%         69.174262\n",
              "max         78.998742\n",
              "Name: Height, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Height</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>66.367560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.847528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>54.263133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>63.505620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>66.318070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>69.174262</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>78.998742</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weight.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "hZLR96gP34ED",
        "outputId": "44d63260-f8a5-4234-b83b-d637469ec89c"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    10000.000000\n",
              "mean       161.440357\n",
              "std         32.108439\n",
              "min         64.700127\n",
              "25%        135.818051\n",
              "50%        161.212928\n",
              "75%        187.169525\n",
              "max        269.989699\n",
              "Name: Weight, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>161.440357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>32.108439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>64.700127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>135.818051</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>161.212928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>187.169525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>269.989699</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Modifying a dataframe\n",
        "\n",
        "data = [\n",
        "    { 'name' : 'Asabeneh', 'country' : 'Finland', 'city' : 'Helsink'},\n",
        "    { 'name' : 'David', 'country' : 'UK', 'city' : 'London'},\n",
        "    { 'name' : 'John', 'country' : 'Sweden', 'city' : 'Stockholm'},\n",
        "\n",
        "]\n",
        "\n",
        "data_frame = pd.DataFrame(data)\n",
        "print(data_frame)\n",
        "\n",
        "\n",
        "data_frame.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A5_zupuQ4Mez",
        "outputId": "e0d7b125-c03e-4a44-89f4-5c420f95321f"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       name  country       city\n",
            "0  Asabeneh  Finland    Helsink\n",
            "1     David       UK     London\n",
            "2      John   Sweden  Stockholm\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['name', 'country', 'city'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Adding a new column\n",
        "\n",
        "\n",
        "weight = [50,60,70]\n",
        "data_frame['weight'] = weight\n",
        "\n",
        "height = [150,160,170]\n",
        "data_frame['height'] = height\n",
        "\n",
        "\n",
        "\n",
        "data_frame"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "0k2VG6zF43bM",
        "outputId": "93e9a039-861c-4f86-d792-d4b5ae743995"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       name  country       city  weight  height\n",
              "0  Asabeneh  Finland    Helsink      50     150\n",
              "1     David       UK     London      60     160\n",
              "2      John   Sweden  Stockholm      70     170"
            ],
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              "\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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              "      <th>country</th>\n",
              "      <th>city</th>\n",
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              "      <th>0</th>\n",
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              "      <td>160</td>\n",
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              "      <td>John</td>\n",
              "      <td>Sweden</td>\n",
              "      <td>Stockholm</td>\n",
              "      <td>70</td>\n",
              "      <td>170</td>\n",
              "    </tr>\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-40d28384-5768-4cef-b374-85d6bd97f93c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    .colab-df-buttons div {\n",
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              "    [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",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-40d28384-5768-4cef-b374-85d6bd97f93c 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-40d28384-5768-4cef-b374-85d6bd97f93c');\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",
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              "            title=\"Suggest charts\"\n",
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              "    </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-59f4fd29-0214-46ea-a580-b89076d167f2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_cfe73875-deca-449d-90a5-eda08b4b7b21\">\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('data_frame')\"\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_cfe73875-deca-449d-90a5-eda08b4b7b21 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data_frame');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_frame",
              "summary": "{\n  \"name\": \"data_frame\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Asabeneh\",\n          \"David\",\n          \"John\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Finland\",\n          \"UK\",\n          \"Sweden\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Helsink\",\n          \"London\",\n          \"Stockholm\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 50,\n        \"max\": 70,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          50,\n          60,\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 150,\n        \"max\": 170,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          150,\n          160,\n          170\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Deleting a label from the dataframe\n",
        "\n",
        "delete = [1,2,3]\n",
        "data_frame['delete'] = delete\n",
        "print(data_frame)\n",
        "\n",
        "data_frame.drop(labels = 'delete',axis = 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        },
        "id": "LF9sp4Xe6afk",
        "outputId": "bf00ab15-6240-4ea2-b180-154286acfaa2"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       name  country       city  weight  height       bml  delete       bmi\n",
            "0  Asabeneh  Finland    Helsink      50     150  0.002222       1  0.002222\n",
            "1     David       UK     London      60     160  0.002344       2  0.002344\n",
            "2      John   Sweden  Stockholm      70     170  0.002422       3  0.002422\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       name  country       city  weight  height       bml       bmi\n",
              "0  Asabeneh  Finland    Helsink      50     150  0.002222  0.002222\n",
              "1     David       UK     London      60     160  0.002344  0.002344\n",
              "2      John   Sweden  Stockholm      70     170  0.002422  0.002422"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c45fdf8e-2f7e-4f55-9dc7-a12f41608c8e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>country</th>\n",
              "      <th>city</th>\n",
              "      <th>weight</th>\n",
              "      <th>height</th>\n",
              "      <th>bml</th>\n",
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              "      <td>0.002222</td>\n",
              "      <td>0.002222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>David</td>\n",
              "      <td>UK</td>\n",
              "      <td>London</td>\n",
              "      <td>60</td>\n",
              "      <td>160</td>\n",
              "      <td>0.002344</td>\n",
              "      <td>0.002344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>John</td>\n",
              "      <td>Sweden</td>\n",
              "      <td>Stockholm</td>\n",
              "      <td>70</td>\n",
              "      <td>170</td>\n",
              "      <td>0.002422</td>\n",
              "      <td>0.002422</td>\n",
              "    </tr>\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-c45fdf8e-2f7e-4f55-9dc7-a12f41608c8e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\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-c45fdf8e-2f7e-4f55-9dc7-a12f41608c8e 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-c45fdf8e-2f7e-4f55-9dc7-a12f41608c8e');\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 id=\"df-ccae2514-dab8-4674-9499-ddd28ab7d1f2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ccae2514-dab8-4674-9499-ddd28ab7d1f2')\"\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",
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              "    </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-ccae2514-dab8-4674-9499-ddd28ab7d1f2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
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              "summary": "{\n  \"name\": \"data_frame\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Asabeneh\",\n          \"David\",\n          \"John\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Finland\",\n          \"UK\",\n          \"Sweden\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Helsink\",\n          \"London\",\n          \"Stockholm\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 50,\n        \"max\": 70,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          50,\n          60,\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 150,\n        \"max\": 170,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          150,\n          160,\n          170\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bml\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00010073403665783261,\n        \"min\": 0.0022222222222222222,\n        \"max\": 0.002422145328719723,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0022222222222222222,\n          0.00234375,\n          0.002422145328719723\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00010073403665783261,\n        \"min\": 0.0022222222222222222,\n        \"max\": 0.002422145328719723,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0022222222222222222,\n          0.00234375,\n          0.002422145328719723\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Modifying column values\n",
        "\n",
        "\n",
        "#Calculating bmi\n",
        "def BMI():\n",
        "  weight = data_frame['weight']\n",
        "  height = data_frame['height']\n",
        "  return weight/(height**2)\n",
        "\n",
        "res = BMI()\n",
        "\n",
        "\n",
        "#adding it to the dataframe\n",
        "\n",
        "data_frame['bmi'] = res\n",
        "data_frame\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "PdmXl38I6gz7",
        "outputId": "62bcfc6b-0eae-453c-81d2-9aa076782ec0"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       name  country       city  weight  height       bml  delete       bmi\n",
              "0  Asabeneh  Finland    Helsink      50     150  0.002222       1  0.002222\n",
              "1     David       UK     London      60     160  0.002344       2  0.002344\n",
              "2      John   Sweden  Stockholm      70     170  0.002422       3  0.002422"
            ],
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              "      <th>weight</th>\n",
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              "      <th>delete</th>\n",
              "      <th>bmi</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Asabeneh</td>\n",
              "      <td>Finland</td>\n",
              "      <td>Helsink</td>\n",
              "      <td>50</td>\n",
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              "      <td>0.002222</td>\n",
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              "      <th>1</th>\n",
              "      <td>David</td>\n",
              "      <td>UK</td>\n",
              "      <td>London</td>\n",
              "      <td>60</td>\n",
              "      <td>160</td>\n",
              "      <td>0.002344</td>\n",
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              "      <td>0.002344</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>John</td>\n",
              "      <td>Sweden</td>\n",
              "      <td>Stockholm</td>\n",
              "      <td>70</td>\n",
              "      <td>170</td>\n",
              "      <td>0.002422</td>\n",
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              "      <td>0.002422</td>\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\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 id=\"id_ba5a7c8c-6804-4bb8-8af8-4c925525ff25\">\n",
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              "        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",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data_frame')\"\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",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_frame",
              "summary": "{\n  \"name\": \"data_frame\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Asabeneh\",\n          \"David\",\n          \"John\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Finland\",\n          \"UK\",\n          \"Sweden\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Helsink\",\n          \"London\",\n          \"Stockholm\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 50,\n        \"max\": 70,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          50,\n          60,\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 150,\n        \"max\": 170,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          150,\n          160,\n          170\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bml\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00010073403665783261,\n        \"min\": 0.0022222222222222222,\n        \"max\": 0.002422145328719723,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0022222222222222222,\n          0.00234375,\n          0.002422145328719723\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"delete\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00010073403665783261,\n        \"min\": 0.0022222222222222222,\n        \"max\": 0.002422145328719723,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0022222222222222222,\n          0.00234375,\n          0.002422145328719723\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Formatting dataframe columns\n",
        "\n",
        "#round() method is used\n",
        "\n",
        "data_frame['bmi'] = round(data_frame['bmi'],1)\n",
        "data_frame"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "PNvu2xCP7z-E",
        "outputId": "c3a74419-6b51-4486-cd0a-bdf4a4346739"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       name  country       city  weight  height       bml  delete  bmi\n",
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              "1     David       UK     London      60     160  0.002344       2  0.0\n",
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              "summary": "{\n  \"name\": \"data_frame\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Asabeneh\",\n          \"David\",\n          \"John\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Finland\",\n          \"UK\",\n          \"Sweden\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Helsink\",\n          \"London\",\n          \"Stockholm\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 50,\n        \"max\": 70,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          50,\n          60,\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 150,\n        \"max\": 170,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          150,\n          160,\n          170\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bml\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00010073403665783261,\n        \"min\": 0.0022222222222222222,\n        \"max\": 0.002422145328719723,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0022222222222222222,\n          0.00234375,\n          0.002422145328719723\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"delete\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\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}"
            }
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Adding 2 more columns\n",
        "\n",
        "birth_year = [1990,1999,2000]\n",
        "data_frame['birth_year'] = birth_year\n",
        "\n",
        "current_year = pd.Series(2024,index = [0,1,2])\n",
        "data_frame['current_year'] = current_year\n",
        "data_frame\n",
        "\n",
        "age = data_frame['current_year'] - data_frame['birth_year']\n",
        "data_frame['age'] = age\n",
        "data_frame"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "HYddfEkC8iLc",
        "outputId": "90b54b34-1fe7-4bac-daa2-38b71ecc5507"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       name  country       city  weight  height       bml  delete  bmi  \\\n",
              "0  Asabeneh  Finland    Helsink      50     150  0.002222       1  0.0   \n",
              "1     David       UK     London      60     160  0.002344       2  0.0   \n",
              "2      John   Sweden  Stockholm      70     170  0.002422       3  0.0   \n",
              "\n",
              "   birth_year  current_year  age  \n",
              "0        1990          2024   34  \n",
              "1        1999          2024   25  \n",
              "2        2000          2024   24  "
            ],
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              "      <th>2</th>\n",
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              "      <td>Sweden</td>\n",
              "      <td>Stockholm</td>\n",
              "      <td>70</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>24</td>\n",
              "    </tr>\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-2bdef52e-ba7b-4cf7-8279-f2b7af192ace')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\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-2bdef52e-ba7b-4cf7-8279-f2b7af192ace 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-2bdef52e-ba7b-4cf7-8279-f2b7af192ace');\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",
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              "    </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",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    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",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    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",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\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-4361d5b5-99b6-4e08-b991-d531e119ea0f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_fe27c2ff-9bd9-4a50-8df9-486551093703\">\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('data_frame')\"\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_fe27c2ff-9bd9-4a50-8df9-486551093703 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data_frame');\n",
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              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_frame",
              "summary": "{\n  \"name\": \"data_frame\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Asabeneh\",\n          \"David\",\n          \"John\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Finland\",\n          \"UK\",\n          \"Sweden\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Helsink\",\n          \"London\",\n          \"Stockholm\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 50,\n        \"max\": 70,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          50,\n          60,\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 150,\n        \"max\": 170,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          150,\n          160,\n          170\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bml\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00010073403665783261,\n        \"min\": 0.0022222222222222222,\n        \"max\": 0.002422145328719723,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0022222222222222222,\n          0.00234375,\n          0.002422145328719723\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"delete\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\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\": \"birth_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 1990,\n        \"max\": 2000,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1990\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"current_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2024,\n        \"max\": 2024,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2024\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 24,\n        \"max\": 34,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          34\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking datatype of a label\n",
        "print(data_frame.age.dtype)\n",
        "\n",
        "\n",
        "#Convertig types\n",
        "data_frame.age.astype(float)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "fca_HLFA9di0",
        "outputId": "bab657f1-063c-46c5-deac-449e1257493a"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    34.0\n",
              "1    25.0\n",
              "2    24.0\n",
              "Name: age, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>34.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>25.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Boolean indexing\n",
        "#Syntax => dataframe[condition]\n",
        "\n",
        "data_frame[data_frame['age'] > 25]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "CTlocrGZ-PB0",
        "outputId": "bb4eb98c-7000-4744-e764-ab63e44f37e6"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       name  country     city  weight  height       bml  delete  bmi  \\\n",
              "0  Asabeneh  Finland  Helsink      50     150  0.002222       1  0.0   \n",
              "\n",
              "   birth_year  current_year  age  \n",
              "0        1990          2024   34  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
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              "      <th>0</th>\n",
              "      <td>Asabeneh</td>\n",
              "      <td>Finland</td>\n",
              "      <td>Helsink</td>\n",
              "      <td>50</td>\n",
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              "      <td>0.002222</td>\n",
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            }
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Exercises\n",
        "\n",
        "ds = pd.read_csv('Car data.csv')\n",
        "ds\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 864
        },
        "id": "BuC0-ZXo_x7M",
        "outputId": "ee8a82d7-f837-4eea-fce3-53dd8c8c4655"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Name  Min Price (Lakh)  Max Price (Lakh)  Range (kmpl)  \\\n",
              "0            Mahindra Thar             11.35             17.60         15.20   \n",
              "1             Maruti Swift              6.49              9.60         24.80   \n",
              "2         Mahindra Scorpio             13.62             17.42         22.00   \n",
              "3               Tata Punch              6.13             10.20         20.09   \n",
              "4            Hyundai Creta             11.00             20.15         17.40   \n",
              "5          Mahindra XUV700             13.99             26.04         15.00   \n",
              "6               Tata Nexon              8.00             15.80         17.44   \n",
              "7            Maruti Brezza              8.34             14.14         17.38   \n",
              "8         Mahindra XUV 3XO              7.49             15.49         18.89   \n",
              "9          Toyota Fortuner             33.43             51.44         10.00   \n",
              "10         Mahindra Bolero              9.79             10.91         16.00   \n",
              "11    Toyota Innova Crysta             19.99             26.55         20.00   \n",
              "12           Maruti Ertiga              8.69             13.03         20.51   \n",
              "13     Land Rover Defender             97.00            285.00          9.20   \n",
              "14            Maruti FRONX              7.51             13.04         21.79   \n",
              "15           Maruti Baleno              6.66              9.83         22.35   \n",
              "16  Land Rover Range Rover              2.36            260.00         13.16   \n",
              "17           Hyundai Exter              6.13             10.43         19.40   \n",
              "18            Tata Harrier             14.99             26.44         16.80   \n",
              "19      Mahindra Scorpio N             13.85             24.54         19.00   \n",
              "20              Kia Seltos             10.90             20.37         17.00   \n",
              "\n",
              "      CC  Seats  Variants     Type  Ex-Showroom Price        RTO   Insurance  \\\n",
              "0   1497      4        19   Petrol            1430000   143000.0   84367.000   \n",
              "1   1197      5        11   Petrol             649000    46240.0   27791.000   \n",
              "2   2184      7         4  Diesel             1361600   175000.0   85943.000   \n",
              "3   1199      5        25   Petrol             612900    42903.0   35311.000   \n",
              "4   1497      5        28   Petrol            1099900   116863.0   45300.000   \n",
              "5   1999      5        49   Petrol            1399000   139900.0   83172.000   \n",
              "6   1199      5       100   Petrol             799990    55999.0   42196.000   \n",
              "7   1462      5        15   Petrol             834000    59210.0   33849.000   \n",
              "8   1197      5        25   Petrol             749000    52430.0   40320.000   \n",
              "9   2694      7         7   Petrol            3343000   334300.0  166175.000   \n",
              "10  1493      7         3  Diesel              979400    90497.5   42150.656   \n",
              "11  2393      8         7  Diesel             1999000   249875.0  106309.000   \n",
              "12  1462      7         9   Petrol             869000    61660.0   32520.000   \n",
              "13  1997      5        24   Petrol            9700000   970000.0  403278.000   \n",
              "14  1197      5        16   Petrol             751500    53405.0   30141.000   \n",
              "15  1197      5         9   Petrol             666000    47420.0   33850.000   \n",
              "16  2997      5         2  Diesel            23600000  2950000.0  939295.000   \n",
              "17  1197      5        26   Petrol             612800    50309.0   34300.000   \n",
              "18  1956      5        25  Diesel             1499000   187375.0   87028.000   \n",
              "19  1997      7        34   Petrol            1385199   143320.0   70852.000   \n",
              "20  1497      5        28   Petrol            1089900   108990.0   25916.000   \n",
              "\n",
              "     Other Onroad Price  \n",
              "0    14300    16,71,667  \n",
              "1     5485     7,28,516  \n",
              "2    27532    16,50,075  \n",
              "3        0     6,91,114  \n",
              "4    11599    12,73,662  \n",
              "5    13990    16,36,062  \n",
              "6        0     8,98,185  \n",
              "7     5485     9,32,544  \n",
              "8        0     8,41,750  \n",
              "9    33830    38,77,305  \n",
              "10     300    11,12,348  \n",
              "11   19990    23,75,174  \n",
              "12    5485     9,68,665  \n",
              "13   97000  1,11,70,278  \n",
              "14    4500     8,39,546  \n",
              "15    4500     7,51,770  \n",
              "16  236000  2,77,25,295  \n",
              "17     600     6,98,009  \n",
              "18   14990    17,88,393  \n",
              "19   14151    16,13,522  \n",
              "20   17719    12,42,525  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-604d03c2-e624-4c14-815b-8ff8c56d21e7\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Min Price (Lakh)</th>\n",
              "      <th>Max Price (Lakh)</th>\n",
              "      <th>Range (kmpl)</th>\n",
              "      <th>CC</th>\n",
              "      <th>Seats</th>\n",
              "      <th>Variants</th>\n",
              "      <th>Type</th>\n",
              "      <th>Ex-Showroom Price</th>\n",
              "      <th>RTO</th>\n",
              "      <th>Insurance</th>\n",
              "      <th>Other</th>\n",
              "      <th>Onroad Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mahindra Thar</td>\n",
              "      <td>11.35</td>\n",
              "      <td>17.60</td>\n",
              "      <td>15.20</td>\n",
              "      <td>1497</td>\n",
              "      <td>4</td>\n",
              "      <td>19</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>1430000</td>\n",
              "      <td>143000.0</td>\n",
              "      <td>84367.000</td>\n",
              "      <td>14300</td>\n",
              "      <td>16,71,667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Maruti Swift</td>\n",
              "      <td>6.49</td>\n",
              "      <td>9.60</td>\n",
              "      <td>24.80</td>\n",
              "      <td>1197</td>\n",
              "      <td>5</td>\n",
              "      <td>11</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>649000</td>\n",
              "      <td>46240.0</td>\n",
              "      <td>27791.000</td>\n",
              "      <td>5485</td>\n",
              "      <td>7,28,516</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mahindra Scorpio</td>\n",
              "      <td>13.62</td>\n",
              "      <td>17.42</td>\n",
              "      <td>22.00</td>\n",
              "      <td>2184</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>1361600</td>\n",
              "      <td>175000.0</td>\n",
              "      <td>85943.000</td>\n",
              "      <td>27532</td>\n",
              "      <td>16,50,075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Tata Punch</td>\n",
              "      <td>6.13</td>\n",
              "      <td>10.20</td>\n",
              "      <td>20.09</td>\n",
              "      <td>1199</td>\n",
              "      <td>5</td>\n",
              "      <td>25</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>612900</td>\n",
              "      <td>42903.0</td>\n",
              "      <td>35311.000</td>\n",
              "      <td>0</td>\n",
              "      <td>6,91,114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Hyundai Creta</td>\n",
              "      <td>11.00</td>\n",
              "      <td>20.15</td>\n",
              "      <td>17.40</td>\n",
              "      <td>1497</td>\n",
              "      <td>5</td>\n",
              "      <td>28</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>1099900</td>\n",
              "      <td>116863.0</td>\n",
              "      <td>45300.000</td>\n",
              "      <td>11599</td>\n",
              "      <td>12,73,662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Mahindra XUV700</td>\n",
              "      <td>13.99</td>\n",
              "      <td>26.04</td>\n",
              "      <td>15.00</td>\n",
              "      <td>1999</td>\n",
              "      <td>5</td>\n",
              "      <td>49</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>1399000</td>\n",
              "      <td>139900.0</td>\n",
              "      <td>83172.000</td>\n",
              "      <td>13990</td>\n",
              "      <td>16,36,062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Tata Nexon</td>\n",
              "      <td>8.00</td>\n",
              "      <td>15.80</td>\n",
              "      <td>17.44</td>\n",
              "      <td>1199</td>\n",
              "      <td>5</td>\n",
              "      <td>100</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>799990</td>\n",
              "      <td>55999.0</td>\n",
              "      <td>42196.000</td>\n",
              "      <td>0</td>\n",
              "      <td>8,98,185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Maruti Brezza</td>\n",
              "      <td>8.34</td>\n",
              "      <td>14.14</td>\n",
              "      <td>17.38</td>\n",
              "      <td>1462</td>\n",
              "      <td>5</td>\n",
              "      <td>15</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>834000</td>\n",
              "      <td>59210.0</td>\n",
              "      <td>33849.000</td>\n",
              "      <td>5485</td>\n",
              "      <td>9,32,544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Mahindra XUV 3XO</td>\n",
              "      <td>7.49</td>\n",
              "      <td>15.49</td>\n",
              "      <td>18.89</td>\n",
              "      <td>1197</td>\n",
              "      <td>5</td>\n",
              "      <td>25</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>749000</td>\n",
              "      <td>52430.0</td>\n",
              "      <td>40320.000</td>\n",
              "      <td>0</td>\n",
              "      <td>8,41,750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Toyota Fortuner</td>\n",
              "      <td>33.43</td>\n",
              "      <td>51.44</td>\n",
              "      <td>10.00</td>\n",
              "      <td>2694</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>3343000</td>\n",
              "      <td>334300.0</td>\n",
              "      <td>166175.000</td>\n",
              "      <td>33830</td>\n",
              "      <td>38,77,305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Mahindra Bolero</td>\n",
              "      <td>9.79</td>\n",
              "      <td>10.91</td>\n",
              "      <td>16.00</td>\n",
              "      <td>1493</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>979400</td>\n",
              "      <td>90497.5</td>\n",
              "      <td>42150.656</td>\n",
              "      <td>300</td>\n",
              "      <td>11,12,348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Toyota Innova Crysta</td>\n",
              "      <td>19.99</td>\n",
              "      <td>26.55</td>\n",
              "      <td>20.00</td>\n",
              "      <td>2393</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>1999000</td>\n",
              "      <td>249875.0</td>\n",
              "      <td>106309.000</td>\n",
              "      <td>19990</td>\n",
              "      <td>23,75,174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Maruti Ertiga</td>\n",
              "      <td>8.69</td>\n",
              "      <td>13.03</td>\n",
              "      <td>20.51</td>\n",
              "      <td>1462</td>\n",
              "      <td>7</td>\n",
              "      <td>9</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>869000</td>\n",
              "      <td>61660.0</td>\n",
              "      <td>32520.000</td>\n",
              "      <td>5485</td>\n",
              "      <td>9,68,665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Land Rover Defender</td>\n",
              "      <td>97.00</td>\n",
              "      <td>285.00</td>\n",
              "      <td>9.20</td>\n",
              "      <td>1997</td>\n",
              "      <td>5</td>\n",
              "      <td>24</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>9700000</td>\n",
              "      <td>970000.0</td>\n",
              "      <td>403278.000</td>\n",
              "      <td>97000</td>\n",
              "      <td>1,11,70,278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Maruti FRONX</td>\n",
              "      <td>7.51</td>\n",
              "      <td>13.04</td>\n",
              "      <td>21.79</td>\n",
              "      <td>1197</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>751500</td>\n",
              "      <td>53405.0</td>\n",
              "      <td>30141.000</td>\n",
              "      <td>4500</td>\n",
              "      <td>8,39,546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Maruti Baleno</td>\n",
              "      <td>6.66</td>\n",
              "      <td>9.83</td>\n",
              "      <td>22.35</td>\n",
              "      <td>1197</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>666000</td>\n",
              "      <td>47420.0</td>\n",
              "      <td>33850.000</td>\n",
              "      <td>4500</td>\n",
              "      <td>7,51,770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Land Rover Range Rover</td>\n",
              "      <td>2.36</td>\n",
              "      <td>260.00</td>\n",
              "      <td>13.16</td>\n",
              "      <td>2997</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>23600000</td>\n",
              "      <td>2950000.0</td>\n",
              "      <td>939295.000</td>\n",
              "      <td>236000</td>\n",
              "      <td>2,77,25,295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Hyundai Exter</td>\n",
              "      <td>6.13</td>\n",
              "      <td>10.43</td>\n",
              "      <td>19.40</td>\n",
              "      <td>1197</td>\n",
              "      <td>5</td>\n",
              "      <td>26</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>612800</td>\n",
              "      <td>50309.0</td>\n",
              "      <td>34300.000</td>\n",
              "      <td>600</td>\n",
              "      <td>6,98,009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Tata Harrier</td>\n",
              "      <td>14.99</td>\n",
              "      <td>26.44</td>\n",
              "      <td>16.80</td>\n",
              "      <td>1956</td>\n",
              "      <td>5</td>\n",
              "      <td>25</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>1499000</td>\n",
              "      <td>187375.0</td>\n",
              "      <td>87028.000</td>\n",
              "      <td>14990</td>\n",
              "      <td>17,88,393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Mahindra Scorpio N</td>\n",
              "      <td>13.85</td>\n",
              "      <td>24.54</td>\n",
              "      <td>19.00</td>\n",
              "      <td>1997</td>\n",
              "      <td>7</td>\n",
              "      <td>34</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>1385199</td>\n",
              "      <td>143320.0</td>\n",
              "      <td>70852.000</td>\n",
              "      <td>14151</td>\n",
              "      <td>16,13,522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Kia Seltos</td>\n",
              "      <td>10.90</td>\n",
              "      <td>20.37</td>\n",
              "      <td>17.00</td>\n",
              "      <td>1497</td>\n",
              "      <td>5</td>\n",
              "      <td>28</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>1089900</td>\n",
              "      <td>108990.0</td>\n",
              "      <td>25916.000</td>\n",
              "      <td>17719</td>\n",
              "      <td>12,42,525</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ds",
              "summary": "{\n  \"name\": \"ds\",\n  \"rows\": 21,\n  \"fields\": [\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 21,\n        \"samples\": [\n          \"Mahindra Thar\",\n          \"Hyundai Exter\",\n          \"Maruti Baleno\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Min Price (Lakh)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.83747713414231,\n        \"min\": 2.36,\n        \"max\": 97.0,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          11.35,\n          14.99,\n          6.66\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Max Price (Lakh)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 77.05115243413671,\n        \"min\": 9.6,\n        \"max\": 285.0,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          17.6,\n          10.43,\n          9.83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Range (kmpl)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.8868191181405467,\n        \"min\": 9.2,\n        \"max\": 24.8,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          15.2,\n          19.4,\n          22.35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 536,\n        \"min\": 1197,\n        \"max\": 2997,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          2997,\n          1997,\n          1497\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Seats\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 4,\n        \"max\": 8,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5,\n          8,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Variants\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21,\n        \"min\": 2,\n        \"max\": 100,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          19,\n          11,\n          49\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Diesel\\u00a0\",\n          \"Petrol\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ex-Showroom Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5187113,\n        \"min\": 612800,\n        \"max\": 23600000,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          1430000,\n          612800\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RTO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 641983.9825220646,\n        \"min\": 42903.0,\n        \"max\": 2950000.0,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          143000.0,\n          50309.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insurance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 205833.8110818135,\n        \"min\": 25916.0,\n        \"max\": 939295.0,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          84367.0,\n          34300.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Other\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52708,\n        \"min\": 0,\n        \"max\": 236000,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          14300,\n          5485\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Onroad Price\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 21,\n        \"samples\": [\n          \"16,71,667\",\n          \"6,98,009\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds.head()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "SUbvWgadAJsN",
        "outputId": "30cd99e4-860d-48c9-c857-73e8cc7364a0"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Name  Min Price (Lakh)  Max Price (Lakh)  Range (kmpl)    CC  \\\n",
              "0     Mahindra Thar             11.35             17.60         15.20  1497   \n",
              "1      Maruti Swift              6.49              9.60         24.80  1197   \n",
              "2  Mahindra Scorpio             13.62             17.42         22.00  2184   \n",
              "3        Tata Punch              6.13             10.20         20.09  1199   \n",
              "4     Hyundai Creta             11.00             20.15         17.40  1497   \n",
              "\n",
              "   Seats  Variants     Type  Ex-Showroom Price       RTO  Insurance  Other  \\\n",
              "0      4        19   Petrol            1430000  143000.0    84367.0  14300   \n",
              "1      5        11   Petrol             649000   46240.0    27791.0   5485   \n",
              "2      7         4  Diesel             1361600  175000.0    85943.0  27532   \n",
              "3      5        25   Petrol             612900   42903.0    35311.0      0   \n",
              "4      5        28   Petrol            1099900  116863.0    45300.0  11599   \n",
              "\n",
              "  Onroad Price  \n",
              "0    16,71,667  \n",
              "1     7,28,516  \n",
              "2    16,50,075  \n",
              "3     6,91,114  \n",
              "4    12,73,662  "
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              "      <th>0</th>\n",
              "      <td>Mahindra Thar</td>\n",
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              "      <td>6.13</td>\n",
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              "      <td>6,91,114</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Hyundai Creta</td>\n",
              "      <td>11.00</td>\n",
              "      <td>20.15</td>\n",
              "      <td>17.40</td>\n",
              "      <td>1497</td>\n",
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              "      <td>116863.0</td>\n",
              "      <td>45300.0</td>\n",
              "      <td>11599</td>\n",
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              "summary": "{\n  \"name\": \"ds\",\n  \"rows\": 21,\n  \"fields\": [\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 21,\n        \"samples\": [\n          \"Mahindra Thar\",\n          \"Hyundai Exter\",\n          \"Maruti Baleno\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Min Price (Lakh)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.83747713414231,\n        \"min\": 2.36,\n        \"max\": 97.0,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          11.35,\n          14.99,\n          6.66\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Max Price (Lakh)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 77.05115243413671,\n        \"min\": 9.6,\n        \"max\": 285.0,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          17.6,\n          10.43,\n          9.83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Range (kmpl)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.8868191181405467,\n        \"min\": 9.2,\n        \"max\": 24.8,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          15.2,\n          19.4,\n          22.35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 536,\n        \"min\": 1197,\n        \"max\": 2997,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          2997,\n          1997,\n          1497\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Seats\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 4,\n        \"max\": 8,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5,\n          8,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Variants\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21,\n        \"min\": 2,\n        \"max\": 100,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          19,\n          11,\n          49\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Diesel\\u00a0\",\n          \"Petrol\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ex-Showroom Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5187113,\n        \"min\": 612800,\n        \"max\": 23600000,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          1430000,\n          612800\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RTO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 641983.9825220646,\n        \"min\": 42903.0,\n        \"max\": 2950000.0,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          143000.0,\n          50309.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insurance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 205833.8110818135,\n        \"min\": 25916.0,\n        \"max\": 939295.0,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          84367.0,\n          34300.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Other\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52708,\n        \"min\": 0,\n        \"max\": 236000,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          14300,\n          5485\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Onroad Price\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 21,\n        \"samples\": [\n          \"16,71,667\",\n          \"6,98,009\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "x3WK7UEGAqtU",
        "outputId": "2d9ff69d-4273-4a24-c7ba-a2d1e884f627"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Name  Min Price (Lakh)  Max Price (Lakh)  Range (kmpl)  \\\n",
              "16  Land Rover Range Rover              2.36            260.00         13.16   \n",
              "17           Hyundai Exter              6.13             10.43         19.40   \n",
              "18            Tata Harrier             14.99             26.44         16.80   \n",
              "19      Mahindra Scorpio N             13.85             24.54         19.00   \n",
              "20              Kia Seltos             10.90             20.37         17.00   \n",
              "\n",
              "      CC  Seats  Variants     Type  Ex-Showroom Price        RTO  Insurance  \\\n",
              "16  2997      5         2  Diesel            23600000  2950000.0   939295.0   \n",
              "17  1197      5        26   Petrol             612800    50309.0    34300.0   \n",
              "18  1956      5        25  Diesel             1499000   187375.0    87028.0   \n",
              "19  1997      7        34   Petrol            1385199   143320.0    70852.0   \n",
              "20  1497      5        28   Petrol            1089900   108990.0    25916.0   \n",
              "\n",
              "     Other Onroad Price  \n",
              "16  236000  2,77,25,295  \n",
              "17     600     6,98,009  \n",
              "18   14990    17,88,393  \n",
              "19   14151    16,13,522  \n",
              "20   17719    12,42,525  "
            ],
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Land Rover Range Rover</td>\n",
              "      <td>2.36</td>\n",
              "      <td>260.00</td>\n",
              "      <td>13.16</td>\n",
              "      <td>2997</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>23600000</td>\n",
              "      <td>2950000.0</td>\n",
              "      <td>939295.0</td>\n",
              "      <td>236000</td>\n",
              "      <td>2,77,25,295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Hyundai Exter</td>\n",
              "      <td>6.13</td>\n",
              "      <td>10.43</td>\n",
              "      <td>19.40</td>\n",
              "      <td>1197</td>\n",
              "      <td>5</td>\n",
              "      <td>26</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>612800</td>\n",
              "      <td>50309.0</td>\n",
              "      <td>34300.0</td>\n",
              "      <td>600</td>\n",
              "      <td>6,98,009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Tata Harrier</td>\n",
              "      <td>14.99</td>\n",
              "      <td>26.44</td>\n",
              "      <td>16.80</td>\n",
              "      <td>1956</td>\n",
              "      <td>5</td>\n",
              "      <td>25</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>1499000</td>\n",
              "      <td>187375.0</td>\n",
              "      <td>87028.0</td>\n",
              "      <td>14990</td>\n",
              "      <td>17,88,393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Mahindra Scorpio N</td>\n",
              "      <td>13.85</td>\n",
              "      <td>24.54</td>\n",
              "      <td>19.00</td>\n",
              "      <td>1997</td>\n",
              "      <td>7</td>\n",
              "      <td>34</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>1385199</td>\n",
              "      <td>143320.0</td>\n",
              "      <td>70852.0</td>\n",
              "      <td>14151</td>\n",
              "      <td>16,13,522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Kia Seltos</td>\n",
              "      <td>10.90</td>\n",
              "      <td>20.37</td>\n",
              "      <td>17.00</td>\n",
              "      <td>1497</td>\n",
              "      <td>5</td>\n",
              "      <td>28</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>1089900</td>\n",
              "      <td>108990.0</td>\n",
              "      <td>25916.0</td>\n",
              "      <td>17719</td>\n",
              "      <td>12,42,525</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"ds\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Hyundai Exter\",\n          \"Kia Seltos\",\n          \"Tata Harrier\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Min Price (Lakh)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.321008363083073,\n        \"min\": 2.36,\n        \"max\": 14.99,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          6.13,\n          10.9,\n          14.99\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Max Price (Lakh)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 107.31063940728338,\n        \"min\": 10.43,\n        \"max\": 260.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          10.43,\n          20.37,\n          26.44\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Range (kmpl)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4758675247274433,\n        \"min\": 13.16,\n        \"max\": 19.4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          19.4,\n          17.0,\n          16.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 683,\n        \"min\": 1197,\n        \"max\": 2997,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1197,\n          1497,\n          1956\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Seats\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 5,\n        \"max\": 7,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          7,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Variants\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 2,\n        \"max\": 34,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          26,\n          28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Petrol\",\n          \"Diesel\\u00a0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ex-Showroom Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10047249,\n        \"min\": 612800,\n        \"max\": 23600000,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          612800,\n          1089900\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RTO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1265488.8467109066,\n        \"min\": 50309.0,\n        \"max\": 2950000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          50309.0,\n          108990.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insurance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 396486.5443469677,\n        \"min\": 25916.0,\n        \"max\": 939295.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          34300.0,\n          25916.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Other\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 100455,\n        \"min\": 600,\n        \"max\": 236000,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          600,\n          17719\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Onroad Price\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"6,98,009\",\n          \"12,42,525\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#display cars having seats = 7\n",
        "n_ds = ds[ds['Seats'] == 7]\n",
        "n_ds[['Name','Seats']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "jFifLGKEBEq-",
        "outputId": "9041df96-7135-493a-91e4-da1a6e6a63cf"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Name  Seats\n",
              "2     Mahindra Scorpio      7\n",
              "9      Toyota Fortuner      7\n",
              "10     Mahindra Bolero      7\n",
              "12       Maruti Ertiga      7\n",
              "19  Mahindra Scorpio N      7"
            ],
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              "      <td>Maruti Ertiga</td>\n",
              "      <td>7</td>\n",
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              "      <th>19</th>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"n_ds[['Name','Seats']]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Toyota Fortuner\",\n          \"Mahindra Scorpio N\",\n          \"Mahindra Bolero\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Seats\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 7,\n        \"max\": 7,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculating average price\n",
        "\n",
        "avg = (ds['Min Price (Lakh)'] + ds['Max Price (Lakh)']) /2\n",
        "ds['Average Price (Lakh)'] = avg\n",
        "ds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "hLK3vsZZB7X8",
        "outputId": "edeaeb38-5432-4144-d309-22daf2f584b2"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Name  Min Price (Lakh)  Max Price (Lakh)  Range (kmpl)  \\\n",
              "0            Mahindra Thar             11.35             17.60         15.20   \n",
              "1             Maruti Swift              6.49              9.60         24.80   \n",
              "2         Mahindra Scorpio             13.62             17.42         22.00   \n",
              "3               Tata Punch              6.13             10.20         20.09   \n",
              "4            Hyundai Creta             11.00             20.15         17.40   \n",
              "5          Mahindra XUV700             13.99             26.04         15.00   \n",
              "6               Tata Nexon              8.00             15.80         17.44   \n",
              "7            Maruti Brezza              8.34             14.14         17.38   \n",
              "8         Mahindra XUV 3XO              7.49             15.49         18.89   \n",
              "9          Toyota Fortuner             33.43             51.44         10.00   \n",
              "10         Mahindra Bolero              9.79             10.91         16.00   \n",
              "11    Toyota Innova Crysta             19.99             26.55         20.00   \n",
              "12           Maruti Ertiga              8.69             13.03         20.51   \n",
              "13     Land Rover Defender             97.00            285.00          9.20   \n",
              "14            Maruti FRONX              7.51             13.04         21.79   \n",
              "15           Maruti Baleno              6.66              9.83         22.35   \n",
              "16  Land Rover Range Rover              2.36            260.00         13.16   \n",
              "17           Hyundai Exter              6.13             10.43         19.40   \n",
              "18            Tata Harrier             14.99             26.44         16.80   \n",
              "19      Mahindra Scorpio N             13.85             24.54         19.00   \n",
              "20              Kia Seltos             10.90             20.37         17.00   \n",
              "\n",
              "      CC  Seats  Variants     Type  Ex-Showroom Price        RTO   Insurance  \\\n",
              "0   1497      4        19   Petrol            1430000   143000.0   84367.000   \n",
              "1   1197      5        11   Petrol             649000    46240.0   27791.000   \n",
              "2   2184      7         4  Diesel             1361600   175000.0   85943.000   \n",
              "3   1199      5        25   Petrol             612900    42903.0   35311.000   \n",
              "4   1497      5        28   Petrol            1099900   116863.0   45300.000   \n",
              "5   1999      5        49   Petrol            1399000   139900.0   83172.000   \n",
              "6   1199      5       100   Petrol             799990    55999.0   42196.000   \n",
              "7   1462      5        15   Petrol             834000    59210.0   33849.000   \n",
              "8   1197      5        25   Petrol             749000    52430.0   40320.000   \n",
              "9   2694      7         7   Petrol            3343000   334300.0  166175.000   \n",
              "10  1493      7         3  Diesel              979400    90497.5   42150.656   \n",
              "11  2393      8         7  Diesel             1999000   249875.0  106309.000   \n",
              "12  1462      7         9   Petrol             869000    61660.0   32520.000   \n",
              "13  1997      5        24   Petrol            9700000   970000.0  403278.000   \n",
              "14  1197      5        16   Petrol             751500    53405.0   30141.000   \n",
              "15  1197      5         9   Petrol             666000    47420.0   33850.000   \n",
              "16  2997      5         2  Diesel            23600000  2950000.0  939295.000   \n",
              "17  1197      5        26   Petrol             612800    50309.0   34300.000   \n",
              "18  1956      5        25  Diesel             1499000   187375.0   87028.000   \n",
              "19  1997      7        34   Petrol            1385199   143320.0   70852.000   \n",
              "20  1497      5        28   Petrol            1089900   108990.0   25916.000   \n",
              "\n",
              "     Other Onroad Price  Average Price (Lakh)  \n",
              "0    14300    16,71,667                14.475  \n",
              "1     5485     7,28,516                 8.045  \n",
              "2    27532    16,50,075                15.520  \n",
              "3        0     6,91,114                 8.165  \n",
              "4    11599    12,73,662                15.575  \n",
              "5    13990    16,36,062                20.015  \n",
              "6        0     8,98,185                11.900  \n",
              "7     5485     9,32,544                11.240  \n",
              "8        0     8,41,750                11.490  \n",
              "9    33830    38,77,305                42.435  \n",
              "10     300    11,12,348                10.350  \n",
              "11   19990    23,75,174                23.270  \n",
              "12    5485     9,68,665                10.860  \n",
              "13   97000  1,11,70,278               191.000  \n",
              "14    4500     8,39,546                10.275  \n",
              "15    4500     7,51,770                 8.245  \n",
              "16  236000  2,77,25,295               131.180  \n",
              "17     600     6,98,009                 8.280  \n",
              "18   14990    17,88,393                20.715  \n",
              "19   14151    16,13,522                19.195  \n",
              "20   17719    12,42,525                15.635  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6fbb3a93-7ca8-4d6f-8365-a472e4c2e910\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Min Price (Lakh)</th>\n",
              "      <th>Max Price (Lakh)</th>\n",
              "      <th>Range (kmpl)</th>\n",
              "      <th>CC</th>\n",
              "      <th>Seats</th>\n",
              "      <th>Variants</th>\n",
              "      <th>Type</th>\n",
              "      <th>Ex-Showroom Price</th>\n",
              "      <th>RTO</th>\n",
              "      <th>Insurance</th>\n",
              "      <th>Other</th>\n",
              "      <th>Onroad Price</th>\n",
              "      <th>Average Price (Lakh)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mahindra Thar</td>\n",
              "      <td>11.35</td>\n",
              "      <td>17.60</td>\n",
              "      <td>15.20</td>\n",
              "      <td>1497</td>\n",
              "      <td>4</td>\n",
              "      <td>19</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>1430000</td>\n",
              "      <td>143000.0</td>\n",
              "      <td>84367.000</td>\n",
              "      <td>14300</td>\n",
              "      <td>16,71,667</td>\n",
              "      <td>14.475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Maruti Swift</td>\n",
              "      <td>6.49</td>\n",
              "      <td>9.60</td>\n",
              "      <td>24.80</td>\n",
              "      <td>1197</td>\n",
              "      <td>5</td>\n",
              "      <td>11</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>649000</td>\n",
              "      <td>46240.0</td>\n",
              "      <td>27791.000</td>\n",
              "      <td>5485</td>\n",
              "      <td>7,28,516</td>\n",
              "      <td>8.045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mahindra Scorpio</td>\n",
              "      <td>13.62</td>\n",
              "      <td>17.42</td>\n",
              "      <td>22.00</td>\n",
              "      <td>2184</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>1361600</td>\n",
              "      <td>175000.0</td>\n",
              "      <td>85943.000</td>\n",
              "      <td>27532</td>\n",
              "      <td>16,50,075</td>\n",
              "      <td>15.520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Tata Punch</td>\n",
              "      <td>6.13</td>\n",
              "      <td>10.20</td>\n",
              "      <td>20.09</td>\n",
              "      <td>1199</td>\n",
              "      <td>5</td>\n",
              "      <td>25</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>612900</td>\n",
              "      <td>42903.0</td>\n",
              "      <td>35311.000</td>\n",
              "      <td>0</td>\n",
              "      <td>6,91,114</td>\n",
              "      <td>8.165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Hyundai Creta</td>\n",
              "      <td>11.00</td>\n",
              "      <td>20.15</td>\n",
              "      <td>17.40</td>\n",
              "      <td>1497</td>\n",
              "      <td>5</td>\n",
              "      <td>28</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>1099900</td>\n",
              "      <td>116863.0</td>\n",
              "      <td>45300.000</td>\n",
              "      <td>11599</td>\n",
              "      <td>12,73,662</td>\n",
              "      <td>15.575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Mahindra XUV700</td>\n",
              "      <td>13.99</td>\n",
              "      <td>26.04</td>\n",
              "      <td>15.00</td>\n",
              "      <td>1999</td>\n",
              "      <td>5</td>\n",
              "      <td>49</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>1399000</td>\n",
              "      <td>139900.0</td>\n",
              "      <td>83172.000</td>\n",
              "      <td>13990</td>\n",
              "      <td>16,36,062</td>\n",
              "      <td>20.015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Tata Nexon</td>\n",
              "      <td>8.00</td>\n",
              "      <td>15.80</td>\n",
              "      <td>17.44</td>\n",
              "      <td>1199</td>\n",
              "      <td>5</td>\n",
              "      <td>100</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>799990</td>\n",
              "      <td>55999.0</td>\n",
              "      <td>42196.000</td>\n",
              "      <td>0</td>\n",
              "      <td>8,98,185</td>\n",
              "      <td>11.900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Maruti Brezza</td>\n",
              "      <td>8.34</td>\n",
              "      <td>14.14</td>\n",
              "      <td>17.38</td>\n",
              "      <td>1462</td>\n",
              "      <td>5</td>\n",
              "      <td>15</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>834000</td>\n",
              "      <td>59210.0</td>\n",
              "      <td>33849.000</td>\n",
              "      <td>5485</td>\n",
              "      <td>9,32,544</td>\n",
              "      <td>11.240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Mahindra XUV 3XO</td>\n",
              "      <td>7.49</td>\n",
              "      <td>15.49</td>\n",
              "      <td>18.89</td>\n",
              "      <td>1197</td>\n",
              "      <td>5</td>\n",
              "      <td>25</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>749000</td>\n",
              "      <td>52430.0</td>\n",
              "      <td>40320.000</td>\n",
              "      <td>0</td>\n",
              "      <td>8,41,750</td>\n",
              "      <td>11.490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Toyota Fortuner</td>\n",
              "      <td>33.43</td>\n",
              "      <td>51.44</td>\n",
              "      <td>10.00</td>\n",
              "      <td>2694</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>3343000</td>\n",
              "      <td>334300.0</td>\n",
              "      <td>166175.000</td>\n",
              "      <td>33830</td>\n",
              "      <td>38,77,305</td>\n",
              "      <td>42.435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Mahindra Bolero</td>\n",
              "      <td>9.79</td>\n",
              "      <td>10.91</td>\n",
              "      <td>16.00</td>\n",
              "      <td>1493</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>979400</td>\n",
              "      <td>90497.5</td>\n",
              "      <td>42150.656</td>\n",
              "      <td>300</td>\n",
              "      <td>11,12,348</td>\n",
              "      <td>10.350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Toyota Innova Crysta</td>\n",
              "      <td>19.99</td>\n",
              "      <td>26.55</td>\n",
              "      <td>20.00</td>\n",
              "      <td>2393</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>1999000</td>\n",
              "      <td>249875.0</td>\n",
              "      <td>106309.000</td>\n",
              "      <td>19990</td>\n",
              "      <td>23,75,174</td>\n",
              "      <td>23.270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Maruti Ertiga</td>\n",
              "      <td>8.69</td>\n",
              "      <td>13.03</td>\n",
              "      <td>20.51</td>\n",
              "      <td>1462</td>\n",
              "      <td>7</td>\n",
              "      <td>9</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>869000</td>\n",
              "      <td>61660.0</td>\n",
              "      <td>32520.000</td>\n",
              "      <td>5485</td>\n",
              "      <td>9,68,665</td>\n",
              "      <td>10.860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Land Rover Defender</td>\n",
              "      <td>97.00</td>\n",
              "      <td>285.00</td>\n",
              "      <td>9.20</td>\n",
              "      <td>1997</td>\n",
              "      <td>5</td>\n",
              "      <td>24</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>9700000</td>\n",
              "      <td>970000.0</td>\n",
              "      <td>403278.000</td>\n",
              "      <td>97000</td>\n",
              "      <td>1,11,70,278</td>\n",
              "      <td>191.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Maruti FRONX</td>\n",
              "      <td>7.51</td>\n",
              "      <td>13.04</td>\n",
              "      <td>21.79</td>\n",
              "      <td>1197</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>751500</td>\n",
              "      <td>53405.0</td>\n",
              "      <td>30141.000</td>\n",
              "      <td>4500</td>\n",
              "      <td>8,39,546</td>\n",
              "      <td>10.275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Maruti Baleno</td>\n",
              "      <td>6.66</td>\n",
              "      <td>9.83</td>\n",
              "      <td>22.35</td>\n",
              "      <td>1197</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>666000</td>\n",
              "      <td>47420.0</td>\n",
              "      <td>33850.000</td>\n",
              "      <td>4500</td>\n",
              "      <td>7,51,770</td>\n",
              "      <td>8.245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Land Rover Range Rover</td>\n",
              "      <td>2.36</td>\n",
              "      <td>260.00</td>\n",
              "      <td>13.16</td>\n",
              "      <td>2997</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>23600000</td>\n",
              "      <td>2950000.0</td>\n",
              "      <td>939295.000</td>\n",
              "      <td>236000</td>\n",
              "      <td>2,77,25,295</td>\n",
              "      <td>131.180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Hyundai Exter</td>\n",
              "      <td>6.13</td>\n",
              "      <td>10.43</td>\n",
              "      <td>19.40</td>\n",
              "      <td>1197</td>\n",
              "      <td>5</td>\n",
              "      <td>26</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>612800</td>\n",
              "      <td>50309.0</td>\n",
              "      <td>34300.000</td>\n",
              "      <td>600</td>\n",
              "      <td>6,98,009</td>\n",
              "      <td>8.280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Tata Harrier</td>\n",
              "      <td>14.99</td>\n",
              "      <td>26.44</td>\n",
              "      <td>16.80</td>\n",
              "      <td>1956</td>\n",
              "      <td>5</td>\n",
              "      <td>25</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>1499000</td>\n",
              "      <td>187375.0</td>\n",
              "      <td>87028.000</td>\n",
              "      <td>14990</td>\n",
              "      <td>17,88,393</td>\n",
              "      <td>20.715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Mahindra Scorpio N</td>\n",
              "      <td>13.85</td>\n",
              "      <td>24.54</td>\n",
              "      <td>19.00</td>\n",
              "      <td>1997</td>\n",
              "      <td>7</td>\n",
              "      <td>34</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>1385199</td>\n",
              "      <td>143320.0</td>\n",
              "      <td>70852.000</td>\n",
              "      <td>14151</td>\n",
              "      <td>16,13,522</td>\n",
              "      <td>19.195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Kia Seltos</td>\n",
              "      <td>10.90</td>\n",
              "      <td>20.37</td>\n",
              "      <td>17.00</td>\n",
              "      <td>1497</td>\n",
              "      <td>5</td>\n",
              "      <td>28</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>1089900</td>\n",
              "      <td>108990.0</td>\n",
              "      <td>25916.000</td>\n",
              "      <td>17719</td>\n",
              "      <td>12,42,525</td>\n",
              "      <td>15.635</td>\n",
              "    </tr>\n",
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\"Seats\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 4,\n        \"max\": 8,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5,\n          8,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Variants\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21,\n        \"min\": 2,\n        \"max\": 100,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          19,\n          11,\n          49\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Diesel\\u00a0\",\n          \"Petrol\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ex-Showroom Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5187113,\n        \"min\": 612800,\n        \"max\": 23600000,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          1430000,\n          612800\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RTO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 641983.9825220646,\n        \"min\": 42903.0,\n        \"max\": 2950000.0,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          143000.0,\n          50309.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insurance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 205833.8110818135,\n        \"min\": 25916.0,\n        \"max\": 939295.0,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          84367.0,\n          34300.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Other\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52708,\n        \"min\": 0,\n        \"max\": 236000,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          14300,\n          5485\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Onroad Price\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 21,\n        \"samples\": [\n          \"16,71,667\",\n          \"6,98,009\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Average Price (Lakh)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45.59073679540197,\n        \"min\": 8.045,\n        \"max\": 191.0,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          14.475000000000001,\n          8.28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    }
  ]
}