missng_nd_outlier_on_daimond by ms.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/diamonds-null.csv\")\n",
        "df"
      ],
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      "execution_count": 10,
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              "       carat        cut color clarity  table  price     x     y     z  \\\n",
              "0       0.23      Ideal     E     SI2   55.0    326  3.95  3.98  2.43   \n",
              "1       0.21    Premium     E     SI1   61.0    326  3.89  3.84  2.31   \n",
              "2       0.23       Good     E     VS1   65.0    327  4.05  4.07  2.31   \n",
              "3       0.29    Premium     I     VS2   58.0    334  4.20  4.23  2.63   \n",
              "4       0.31       Good     J     SI2   58.0    335  4.34  4.35  2.75   \n",
              "...      ...        ...   ...     ...    ...    ...   ...   ...   ...   \n",
              "53935   0.72      Ideal     D     SI1   57.0   2757  5.75  5.76  3.50   \n",
              "53936   0.72       Good     D     SI1   55.0   2757  5.69  5.75  3.61   \n",
              "53937   0.70  Very Good     D     SI1   60.0   2757  5.66  5.68  3.56   \n",
              "53938   0.86    Premium     H     SI2   58.0   2757  6.15  6.12  3.74   \n",
              "53939   0.75      Ideal     D     SI2   55.0   2757  5.83  5.87  3.64   \n",
              "\n",
              "       price_new  \n",
              "0          163.0  \n",
              "1          163.0  \n",
              "2          163.5  \n",
              "3          167.0  \n",
              "4          167.5  \n",
              "...          ...  \n",
              "53935     1378.5  \n",
              "53936     1378.5  \n",
              "53937     1378.5  \n",
              "53938     1378.5  \n",
              "53939     1378.5  \n",
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              "      })();\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 53940,\n  \"fields\": [\n    {\n      \"column\": \"carat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4744282233713494,\n        \"min\": 0.2,\n        \"max\": 5.01,\n        \"num_unique_values\": 273,\n        \"samples\": [\n          0.77,\n          1.58,\n          0.65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cut\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Premium\",\n          \"Fair\",\n          \"Good\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"E\",\n          \"I\",\n          \"G\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clarity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"SI1\",\n          \"VVS1\",\n          \"SI2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"table\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.2357419099749305,\n        \"min\": 43.0,\n        \"max\": 95.0,\n        \"num_unique_values\": 127,\n        \"samples\": [\n          54.8,\n          61.5,\n          57.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3989,\n        \"min\": 326,\n        \"max\": 18823,\n        \"num_unique_values\": 11602,\n        \"samples\": [\n          16368,\n          895,\n          748\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1217607467924928,\n        \"min\": 0.0,\n        \"max\": 10.74,\n        \"num_unique_values\": 554,\n        \"samples\": [\n          5.88,\n          5.69,\n          4.61\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1421346741235552,\n        \"min\": 0.0,\n        \"max\": 58.9,\n        \"num_unique_values\": 552,\n        \"samples\": [\n          5.33,\n          5.61,\n          5.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"z\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7038692080306131,\n        \"min\": 0.0,\n        \"max\": 31.8,\n        \"num_unique_values\": 376,\n        \"samples\": [\n          4.89,\n          6.72,\n          5.06\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_new\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1994.7198690731896,\n        \"min\": 163.0,\n        \"max\": 9411.5,\n        \"num_unique_values\": 11602,\n        \"samples\": [\n          8184.0,\n          447.5,\n          374.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "IDSu8tEiQzU8",
        "outputId": "cbf44e4d-62d0-4771-851d-efecaae3acad"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   carat        cut color clarity  table  price     x     y     z  price_new\n",
              "0   0.23      Ideal     E     SI2   55.0    326  3.95  3.98  2.43      163.0\n",
              "1   0.21    Premium     E     SI1   61.0    326  3.89  3.84  2.31      163.0\n",
              "2   0.23       Good     E     VS1   65.0    327  4.05  4.07  2.31      163.5\n",
              "3   0.29    Premium     I     VS2   58.0    334  4.20  4.23  2.63      167.0\n",
              "4   0.31       Good     J     SI2   58.0    335  4.34  4.35  2.75      167.5\n",
              "5   0.24  Very Good     J    VVS2   57.0    336  3.94  3.96  2.48      168.0\n",
              "6   0.24  Very Good     I    VVS1   57.0    336  3.95  3.98  2.47      168.0\n",
              "7   0.26  Very Good     H     SI1   55.0    337  4.07  4.11  2.53      168.5\n",
              "8   0.22       Fair     E     VS2   61.0    337  3.87  3.78  2.49      168.5\n",
              "9   0.23  Very Good     H     VS1   61.0    338  4.00  4.05  2.39      169.0"
            ],
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              "      <th>carat</th>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
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              "    async function quickchart(key) {\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 53940,\n  \"fields\": [\n    {\n      \"column\": \"carat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4744282233713494,\n        \"min\": 0.2,\n        \"max\": 5.01,\n        \"num_unique_values\": 273,\n        \"samples\": [\n          0.77,\n          1.58,\n          0.65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cut\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Premium\",\n          \"Fair\",\n          \"Good\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"E\",\n          \"I\",\n          \"G\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clarity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"SI1\",\n          \"VVS1\",\n          \"SI2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"table\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.2357419099749305,\n        \"min\": 43.0,\n        \"max\": 95.0,\n        \"num_unique_values\": 127,\n        \"samples\": [\n          54.8,\n          61.5,\n          57.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3989,\n        \"min\": 326,\n        \"max\": 18823,\n        \"num_unique_values\": 11602,\n        \"samples\": [\n          16368,\n          895,\n          748\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1217607467924928,\n        \"min\": 0.0,\n        \"max\": 10.74,\n        \"num_unique_values\": 554,\n        \"samples\": [\n          5.88,\n          5.69,\n          4.61\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1421346741235552,\n        \"min\": 0.0,\n        \"max\": 58.9,\n        \"num_unique_values\": 552,\n        \"samples\": [\n          5.33,\n          5.61,\n          5.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"z\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7038692080306131,\n        \"min\": 0.0,\n        \"max\": 31.8,\n        \"num_unique_values\": 376,\n        \"samples\": [\n          4.89,\n          6.72,\n          5.06\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_new\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1994.7198690731896,\n        \"min\": 163.0,\n        \"max\": 9411.5,\n        \"num_unique_values\": 11602,\n        \"samples\": [\n          8184.0,\n          447.5,\n          374.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KMcLDN_MQ4Od",
        "outputId": "7601da64-ab0c-4eaf-d5a1-761621fd3e3b"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['carat', 'cut', 'color', 'clarity', 'table', 'price', 'x', 'y', 'z',\n",
              "       'price_new'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SsIdz-RXQ6zv",
        "outputId": "a422f863-aecf-4f8d-85a2-6d0c6edb2135"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 53940 entries, 0 to 53939\n",
            "Data columns (total 10 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   carat      53841 non-null  float64\n",
            " 1   cut        53940 non-null  object \n",
            " 2   color      53884 non-null  object \n",
            " 3   clarity    53940 non-null  object \n",
            " 4   table      53877 non-null  float64\n",
            " 5   price      53940 non-null  int64  \n",
            " 6   x          53940 non-null  float64\n",
            " 7   y          53940 non-null  float64\n",
            " 8   z          53940 non-null  float64\n",
            " 9   price_new  53940 non-null  float64\n",
            "dtypes: float64(6), int64(1), object(3)\n",
            "memory usage: 4.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n1EGqjVjRbP3",
        "outputId": "1ece96c9-59d6-4c64-8076-80aed96b85e5"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "carat        float64\n",
              "cut           object\n",
              "color         object\n",
              "clarity       object\n",
              "table        float64\n",
              "price          int64\n",
              "x            float64\n",
              "y            float64\n",
              "z            float64\n",
              "price_new    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NjAmLq-FRkIr",
        "outputId": "4c7346d4-2378-4e0e-f555-8b190ce5ff5e"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "carat        99\n",
              "cut           0\n",
              "color        56\n",
              "clarity       0\n",
              "table        63\n",
              "price         0\n",
              "x             0\n",
              "y             0\n",
              "z             0\n",
              "price_new     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.select_dtypes(include=['float'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "AhovccadR6pK",
        "outputId": "62cd01a7-d6fa-4fb7-c6bf-6b37ac9e2d1a"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       carat  table     x     y     z  price_new\n",
              "0       0.23   55.0  3.95  3.98  2.43      163.0\n",
              "1       0.21   61.0  3.89  3.84  2.31      163.0\n",
              "2       0.23   65.0  4.05  4.07  2.31      163.5\n",
              "3       0.29   58.0  4.20  4.23  2.63      167.0\n",
              "4       0.31   58.0  4.34  4.35  2.75      167.5\n",
              "...      ...    ...   ...   ...   ...        ...\n",
              "53935   0.72   57.0  5.75  5.76  3.50     1378.5\n",
              "53936   0.72   55.0  5.69  5.75  3.61     1378.5\n",
              "53937   0.70   60.0  5.66  5.68  3.56     1378.5\n",
              "53938   0.86   58.0  6.15  6.12  3.74     1378.5\n",
              "53939   0.75   55.0  5.83  5.87  3.64     1378.5\n",
              "\n",
              "[53940 rows x 6 columns]"
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              "      <td>60.0</td>\n",
              "      <td>5.66</td>\n",
              "      <td>5.68</td>\n",
              "      <td>3.56</td>\n",
              "      <td>1378.5</td>\n",
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              "    <tr>\n",
              "      <th>53938</th>\n",
              "      <td>0.86</td>\n",
              "      <td>58.0</td>\n",
              "      <td>6.15</td>\n",
              "      <td>6.12</td>\n",
              "      <td>3.74</td>\n",
              "      <td>1378.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53939</th>\n",
              "      <td>0.75</td>\n",
              "      <td>55.0</td>\n",
              "      <td>5.83</td>\n",
              "      <td>5.87</td>\n",
              "      <td>3.64</td>\n",
              "      <td>1378.5</td>\n",
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              "</table>\n",
              "<p>53940 rows × 6 columns</p>\n",
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              "        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-0a59dcac-0892-4804-abb9-4156e456cdbb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 53940,\n  \"fields\": [\n    {\n      \"column\": \"carat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4744282233713494,\n        \"min\": 0.2,\n        \"max\": 5.01,\n        \"num_unique_values\": 273,\n        \"samples\": [\n          0.77,\n          1.58,\n          0.65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"table\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.2357419099749305,\n        \"min\": 43.0,\n        \"max\": 95.0,\n        \"num_unique_values\": 127,\n        \"samples\": [\n          54.8,\n          61.5,\n          57.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1217607467924928,\n        \"min\": 0.0,\n        \"max\": 10.74,\n        \"num_unique_values\": 554,\n        \"samples\": [\n          5.88,\n          5.69,\n          4.61\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1421346741235552,\n        \"min\": 0.0,\n        \"max\": 58.9,\n        \"num_unique_values\": 552,\n        \"samples\": [\n          5.33,\n          5.61,\n          5.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"z\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7038692080306131,\n        \"min\": 0.0,\n        \"max\": 31.8,\n        \"num_unique_values\": 376,\n        \"samples\": [\n          4.89,\n          6.72,\n          5.06\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_new\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1994.7198690731896,\n        \"min\": 163.0,\n        \"max\": 9411.5,\n        \"num_unique_values\": 11602,\n        \"samples\": [\n          8184.0,\n          447.5,\n          374.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['carat'],color='m')\n",
        "plt.title('carat')\n",
        "# we use median bcz its have skewness"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "O6JDSzP6T1wB",
        "outputId": "0f3c4b33-0739-415e-9182-18034833ec39"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'carat')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['table'])\n",
        "plt.title('title')\n",
        "# we need to use mean() because it doesnt have skewness\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "tInhoYMfUQ9G",
        "outputId": "54c55ecd-5bb2-4e87-d397-4c64138ab4e0"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'title')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# mode bcz its a object\n",
        "df['color'].fillna(df['color'].mode()[0],inplace=True)\n"
      ],
      "metadata": {
        "id": "rCn5Ik5zUdfl"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gXhF8IXSVJUl",
        "outputId": "2334ef5a-2d07-4679-e34a-6d7a6e02ca43"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "carat        99\n",
              "cut           0\n",
              "color         0\n",
              "clarity       0\n",
              "table        63\n",
              "price         0\n",
              "x             0\n",
              "y             0\n",
              "z             0\n",
              "price_new     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# median\n",
        "df['carat'].fillna(df['carat'].median(),inplace=True)"
      ],
      "metadata": {
        "id": "pa-FHLWAVt-0"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# mean\n",
        "df['table'].fillna(df['table'].mean(),inplace=True)"
      ],
      "metadata": {
        "id": "vgtu3fXrWntT"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rpdlQPvZWzJc",
        "outputId": "4a1d1101-37cf-4508-92d3-9c1783201926"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "carat        0\n",
              "cut          0\n",
              "color        0\n",
              "clarity      0\n",
              "table        0\n",
              "price        0\n",
              "x            0\n",
              "y            0\n",
              "z            0\n",
              "price_new    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "hQBUTeosW4m3",
        "outputId": "49f7adce-f6aa-4283-baa1-8d231730aa1a"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   carat        cut color clarity  table  price     x     y     z  price_new\n",
              "0   0.23      Ideal     E     SI2   55.0    326  3.95  3.98  2.43      163.0\n",
              "1   0.21    Premium     E     SI1   61.0    326  3.89  3.84  2.31      163.0\n",
              "2   0.23       Good     E     VS1   65.0    327  4.05  4.07  2.31      163.5\n",
              "3   0.29    Premium     I     VS2   58.0    334  4.20  4.23  2.63      167.0\n",
              "4   0.31       Good     J     SI2   58.0    335  4.34  4.35  2.75      167.5\n",
              "5   0.24  Very Good     J    VVS2   57.0    336  3.94  3.96  2.48      168.0\n",
              "6   0.24  Very Good     I    VVS1   57.0    336  3.95  3.98  2.47      168.0\n",
              "7   0.26  Very Good     H     SI1   55.0    337  4.07  4.11  2.53      168.5\n",
              "8   0.22       Fair     E     VS2   61.0    337  3.87  3.78  2.49      168.5\n",
              "9   0.23  Very Good     H     VS1   61.0    338  4.00  4.05  2.39      169.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c26c7334-5974-445a-b4e5-3fc016e8cefc\" 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>carat</th>\n",
              "      <th>cut</th>\n",
              "      <th>color</th>\n",
              "      <th>clarity</th>\n",
              "      <th>table</th>\n",
              "      <th>price</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "      <th>price_new</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.23</td>\n",
              "      <td>Ideal</td>\n",
              "      <td>E</td>\n",
              "      <td>SI2</td>\n",
              "      <td>55.0</td>\n",
              "      <td>326</td>\n",
              "      <td>3.95</td>\n",
              "      <td>3.98</td>\n",
              "      <td>2.43</td>\n",
              "      <td>163.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.21</td>\n",
              "      <td>Premium</td>\n",
              "      <td>E</td>\n",
              "      <td>SI1</td>\n",
              "      <td>61.0</td>\n",
              "      <td>326</td>\n",
              "      <td>3.89</td>\n",
              "      <td>3.84</td>\n",
              "      <td>2.31</td>\n",
              "      <td>163.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.23</td>\n",
              "      <td>Good</td>\n",
              "      <td>E</td>\n",
              "      <td>VS1</td>\n",
              "      <td>65.0</td>\n",
              "      <td>327</td>\n",
              "      <td>4.05</td>\n",
              "      <td>4.07</td>\n",
              "      <td>2.31</td>\n",
              "      <td>163.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.29</td>\n",
              "      <td>Premium</td>\n",
              "      <td>I</td>\n",
              "      <td>VS2</td>\n",
              "      <td>58.0</td>\n",
              "      <td>334</td>\n",
              "      <td>4.20</td>\n",
              "      <td>4.23</td>\n",
              "      <td>2.63</td>\n",
              "      <td>167.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.31</td>\n",
              "      <td>Good</td>\n",
              "      <td>J</td>\n",
              "      <td>SI2</td>\n",
              "      <td>58.0</td>\n",
              "      <td>335</td>\n",
              "      <td>4.34</td>\n",
              "      <td>4.35</td>\n",
              "      <td>2.75</td>\n",
              "      <td>167.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.24</td>\n",
              "      <td>Very Good</td>\n",
              "      <td>J</td>\n",
              "      <td>VVS2</td>\n",
              "      <td>57.0</td>\n",
              "      <td>336</td>\n",
              "      <td>3.94</td>\n",
              "      <td>3.96</td>\n",
              "      <td>2.48</td>\n",
              "      <td>168.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.24</td>\n",
              "      <td>Very Good</td>\n",
              "      <td>I</td>\n",
              "      <td>VVS1</td>\n",
              "      <td>57.0</td>\n",
              "      <td>336</td>\n",
              "      <td>3.95</td>\n",
              "      <td>3.98</td>\n",
              "      <td>2.47</td>\n",
              "      <td>168.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.26</td>\n",
              "      <td>Very Good</td>\n",
              "      <td>H</td>\n",
              "      <td>SI1</td>\n",
              "      <td>55.0</td>\n",
              "      <td>337</td>\n",
              "      <td>4.07</td>\n",
              "      <td>4.11</td>\n",
              "      <td>2.53</td>\n",
              "      <td>168.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.22</td>\n",
              "      <td>Fair</td>\n",
              "      <td>E</td>\n",
              "      <td>VS2</td>\n",
              "      <td>61.0</td>\n",
              "      <td>337</td>\n",
              "      <td>3.87</td>\n",
              "      <td>3.78</td>\n",
              "      <td>2.49</td>\n",
              "      <td>168.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.23</td>\n",
              "      <td>Very Good</td>\n",
              "      <td>H</td>\n",
              "      <td>VS1</td>\n",
              "      <td>61.0</td>\n",
              "      <td>338</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.05</td>\n",
              "      <td>2.39</td>\n",
              "      <td>169.0</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-c26c7334-5974-445a-b4e5-3fc016e8cefc')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"https://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-c26c7334-5974-445a-b4e5-3fc016e8cefc 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-c26c7334-5974-445a-b4e5-3fc016e8cefc');\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-e9956bc1-150c-4702-ae5e-5263be960ed8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e9956bc1-150c-4702-ae5e-5263be960ed8')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"https://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-e9956bc1-150c-4702-ae5e-5263be960ed8 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": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 53940,\n  \"fields\": [\n    {\n      \"column\": \"carat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4740112444054184,\n        \"min\": 0.2,\n        \"max\": 5.01,\n        \"num_unique_values\": 273,\n        \"samples\": [\n          0.77,\n          1.58,\n          0.65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cut\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Premium\",\n          \"Fair\",\n          \"Good\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"E\",\n          \"I\",\n          \"G\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clarity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"SI1\",\n          \"VVS1\",\n          \"SI2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"table\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.2344358708141954,\n        \"min\": 43.0,\n        \"max\": 95.0,\n        \"num_unique_values\": 128,\n        \"samples\": [\n          55.7,\n          60.7,\n          52.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3989,\n        \"min\": 326,\n        \"max\": 18823,\n        \"num_unique_values\": 11602,\n        \"samples\": [\n          16368,\n          895,\n          748\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1217607467924928,\n        \"min\": 0.0,\n        \"max\": 10.74,\n        \"num_unique_values\": 554,\n        \"samples\": [\n          5.88,\n          5.69,\n          4.61\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1421346741235552,\n        \"min\": 0.0,\n        \"max\": 58.9,\n        \"num_unique_values\": 552,\n        \"samples\": [\n          5.33,\n          5.61,\n          5.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"z\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7038692080306131,\n        \"min\": 0.0,\n        \"max\": 31.8,\n        \"num_unique_values\": 376,\n        \"samples\": [\n          4.89,\n          6.72,\n          5.06\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_new\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1994.7198690731896,\n        \"min\": 163.0,\n        \"max\": 9411.5,\n        \"num_unique_values\": 11602,\n        \"samples\": [\n          8184.0,\n          447.5,\n          374.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('cut')['price'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lxqg2CdVXLik",
        "outputId": "009baf27-9163-4174-fb0f-1c2f9a3b1556"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cut\n",
              "Fair         4358.757764\n",
              "Good         3928.864452\n",
              "Ideal        3457.541970\n",
              "Premium      4584.257704\n",
              "Very Good    3981.759891\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('cut')['price'].median()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QJ7quN6EXwcz",
        "outputId": "8c917088-ce0b-4504-d622-8b7ea4795d44"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cut\n",
              "Fair         3282.0\n",
              "Good         3050.5\n",
              "Ideal        1810.0\n",
              "Premium      3185.0\n",
              "Very Good    2648.0\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# removing outliers"
      ],
      "metadata": {
        "id": "MA6UqV2HX3xE"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dUaP8W0dYALa",
        "outputId": "b9d5c11c-b1fa-4e9d-c6d5-3c588c8bd3d1"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['carat', 'cut', 'color', 'clarity', 'table', 'price', 'x', 'y', 'z',\n",
              "       'price_new'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "jZ9Dzo16YP56",
        "outputId": "74a8bc10-d055-4a3d-8ec2-ae607c44d334"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   carat      cut color clarity  table  price     x     y     z  price_new\n",
              "0   0.23    Ideal     E     SI2   55.0    326  3.95  3.98  2.43      163.0\n",
              "1   0.21  Premium     E     SI1   61.0    326  3.89  3.84  2.31      163.0\n",
              "2   0.23     Good     E     VS1   65.0    327  4.05  4.07  2.31      163.5\n",
              "3   0.29  Premium     I     VS2   58.0    334  4.20  4.23  2.63      167.0\n",
              "4   0.31     Good     J     SI2   58.0    335  4.34  4.35  2.75      167.5"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-37e1b87f-3035-45e6-94f7-49950d20a607\" 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",
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              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>carat</th>\n",
              "      <th>cut</th>\n",
              "      <th>color</th>\n",
              "      <th>clarity</th>\n",
              "      <th>table</th>\n",
              "      <th>price</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "      <th>price_new</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.23</td>\n",
              "      <td>Ideal</td>\n",
              "      <td>E</td>\n",
              "      <td>SI2</td>\n",
              "      <td>55.0</td>\n",
              "      <td>326</td>\n",
              "      <td>3.95</td>\n",
              "      <td>3.98</td>\n",
              "      <td>2.43</td>\n",
              "      <td>163.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.21</td>\n",
              "      <td>Premium</td>\n",
              "      <td>E</td>\n",
              "      <td>SI1</td>\n",
              "      <td>61.0</td>\n",
              "      <td>326</td>\n",
              "      <td>3.89</td>\n",
              "      <td>3.84</td>\n",
              "      <td>2.31</td>\n",
              "      <td>163.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.23</td>\n",
              "      <td>Good</td>\n",
              "      <td>E</td>\n",
              "      <td>VS1</td>\n",
              "      <td>65.0</td>\n",
              "      <td>327</td>\n",
              "      <td>4.05</td>\n",
              "      <td>4.07</td>\n",
              "      <td>2.31</td>\n",
              "      <td>163.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.29</td>\n",
              "      <td>Premium</td>\n",
              "      <td>I</td>\n",
              "      <td>VS2</td>\n",
              "      <td>58.0</td>\n",
              "      <td>334</td>\n",
              "      <td>4.20</td>\n",
              "      <td>4.23</td>\n",
              "      <td>2.63</td>\n",
              "      <td>167.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.31</td>\n",
              "      <td>Good</td>\n",
              "      <td>J</td>\n",
              "      <td>SI2</td>\n",
              "      <td>58.0</td>\n",
              "      <td>335</td>\n",
              "      <td>4.34</td>\n",
              "      <td>4.35</td>\n",
              "      <td>2.75</td>\n",
              "      <td>167.5</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-37e1b87f-3035-45e6-94f7-49950d20a607')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"https://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-37e1b87f-3035-45e6-94f7-49950d20a607 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-37e1b87f-3035-45e6-94f7-49950d20a607');\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-4f849cc6-e8f5-4320-b58e-5e12406e1edb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4f849cc6-e8f5-4320-b58e-5e12406e1edb')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"https://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-4f849cc6-e8f5-4320-b58e-5e12406e1edb 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": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 53940,\n  \"fields\": [\n    {\n      \"column\": \"carat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4740112444054184,\n        \"min\": 0.2,\n        \"max\": 5.01,\n        \"num_unique_values\": 273,\n        \"samples\": [\n          0.77,\n          1.58,\n          0.65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cut\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Premium\",\n          \"Fair\",\n          \"Good\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"E\",\n          \"I\",\n          \"G\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clarity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"SI1\",\n          \"VVS1\",\n          \"SI2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"table\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.2344358708141954,\n        \"min\": 43.0,\n        \"max\": 95.0,\n        \"num_unique_values\": 128,\n        \"samples\": [\n          55.7,\n          60.7,\n          52.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3989,\n        \"min\": 326,\n        \"max\": 18823,\n        \"num_unique_values\": 11602,\n        \"samples\": [\n          16368,\n          895,\n          748\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1217607467924928,\n        \"min\": 0.0,\n        \"max\": 10.74,\n        \"num_unique_values\": 554,\n        \"samples\": [\n          5.88,\n          5.69,\n          4.61\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1421346741235552,\n        \"min\": 0.0,\n        \"max\": 58.9,\n        \"num_unique_values\": 552,\n        \"samples\": [\n          5.33,\n          5.61,\n          5.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"z\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7038692080306131,\n        \"min\": 0.0,\n        \"max\": 31.8,\n        \"num_unique_values\": 376,\n        \"samples\": [\n          4.89,\n          6.72,\n          5.06\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_new\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1994.7198690731896,\n        \"min\": 163.0,\n        \"max\": 9411.5,\n        \"num_unique_values\": 11602,\n        \"samples\": [\n          8184.0,\n          447.5,\n          374.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplots(2,2,figsize=(10,8))\n",
        "plt.subplot(2,2,1)\n",
        "plt.boxplot(df['carat'])\n",
        "plt.title('carat')\n",
        "plt.subplot(2,2,2)\n",
        "plt.boxplot(df['table'])\n",
        "plt.title('table')\n",
        "plt.subplot(2,2,3)\n",
        "plt.boxplot(df['price'])\n",
        "plt.title('price')\n",
        "plt.subplot(2,2,4)\n",
        "plt.boxplot(df['price_new'])\n",
        "plt.title('price_new')\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        },
        "id": "Iycx5-faYTVy",
        "outputId": "ccd1edde-cccd-4755-99cb-35ed06a1252e"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'price_new')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Q1=np.percentile(df['carat'],25,interpolation='midpoint')\n",
        "Q2=np.percentile(df['carat'],50,interpolation='midpoint')\n",
        "Q3=np.percentile(df['carat'],75,interpolation='midpoint')\n",
        "print(Q1)\n",
        "print(Q2)\n",
        "print(Q3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0JlcxkHea9Wu",
        "outputId": "6e2ac093-5436-41dc-b00b-0b7b5a3c2b94"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.4\n",
            "0.7\n",
            "1.04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "IQR = Q3-Q1\n",
        "IQR"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BfpUhlA0jDKr",
        "outputId": "17853ffd-53f0-4297-da9a-65d750a3c6af"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "upperlimit = Q3+1.5*IQR\n",
        "lowerlimit = Q1-1.5*IQR\n",
        "\n",
        "print(lowerlimit)\n",
        "print(upperlimit)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iXke9lIuj5BW",
        "outputId": "781e6f4a-116b-4dc3-fb80-a8e084628503"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-0.5599999999999999\n",
            "2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "outlier=[]\n",
        "for x in df['carat']:\n",
        "  if((x>upperlimit) or (x<lowerlimit)):\n",
        "   outlier.append(x)\n",
        "print(outlier)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jWTuG_iqlbgv",
        "outputId": "0383035c-8fdb-4d62-ce91-42996797c6a9"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "crt = df['carat']>upperlimit\n",
        "crt"
      ],
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        },
        "id": "ChzaDR6gmffZ",
        "outputId": "feb4c67e-8fb7-4b23-e651-9d3d64836904"
      },
      "execution_count": 43,
      "outputs": [
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          "data": {
            "text/plain": [
              "0        False\n",
              "1        False\n",
              "2        False\n",
              "3        False\n",
              "4        False\n",
              "         ...  \n",
              "53935    False\n",
              "53936    False\n",
              "53937    False\n",
              "53938    False\n",
              "53939    False\n",
              "Name: carat, Length: 53940, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
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      "execution_count": 44,
      "outputs": [
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              "       carat      cut color clarity  table  price     x     y     z  price_new\n",
              "12246   2.06  Premium     J      I1   58.0   5203  8.10  8.07  4.95     2601.5\n",
              "13002   2.14     Fair     J      I1   57.0   5405  7.74  7.70  5.36     2702.5\n",
              "13118   2.15     Fair     J      I1   57.0   5430  8.01  7.95  5.23     2715.0\n",
              "13757   2.22     Fair     J      I1   56.0   5607  8.04  8.02  5.36     2803.5\n",
              "13991   2.01     Fair     I      I1   58.0   5696  7.71  7.64  5.17     2848.0\n",
              "...      ...      ...   ...     ...    ...    ...   ...   ...   ...        ...\n",
              "27741   2.15    Ideal     G     SI2   54.0  18791  8.29  8.35  5.21     9395.5\n",
              "27742   2.04  Premium     H     SI1   60.0  18795  8.37  8.28  4.84     9397.5\n",
              "27744   2.29  Premium     I     SI1   59.0  18797  8.52  8.45  5.24     9398.5\n",
              "27746   2.07    Ideal     G     SI2   55.0  18804  8.20  8.13  5.11     9402.0\n",
              "27749   2.29  Premium     I     VS2   60.0  18823  8.50  8.47  5.16     9411.5\n",
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              "    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-963a8210-2a7c-4965-8741-d4c1e74d6db3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1889,\n  \"fields\": [\n    {\n      \"column\": \"carat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23670423773749327,\n        \"min\": 2.01,\n        \"max\": 5.01,\n        \"num_unique_values\": 92,\n        \"samples\": [\n          2.77,\n          2.31,\n          2.13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cut\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Fair\",\n          \"Ideal\",\n          \"Good\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"J\",\n          \"I\",\n          \"E\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clarity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"SI1\",\n          \"VVS1\",\n          \"I1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"table\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.366632291771531,\n        \"min\": 51.0,\n        \"max\": 95.0,\n        \"num_unique_values\": 37,\n        \"samples\": [\n          54.2,\n          56.9,\n          55.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2703,\n        \"min\": 5203,\n        \"max\": 18823,\n        \"num_unique_values\": 1528,\n        \"samples\": [\n          13596,\n          9802,\n          17063\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.35017739344851595,\n        \"min\": 0.0,\n        \"max\": 10.74,\n        \"num_unique_values\": 160,\n        \"samples\": [\n          9.65,\n          8.79,\n          8.73\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.34389719227660226,\n        \"min\": 0.0,\n        \"max\": 10.54,\n        \"num_unique_values\": 159,\n        \"samples\": [\n          7.69,\n          9.63,\n          9.19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"z\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.243410208427677,\n        \"min\": 0.0,\n        \"max\": 6.98,\n        \"num_unique_values\": 126,\n        \"samples\": [\n          4.98,\n          4.79,\n          5.52\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_new\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1351.797264124611,\n        \"min\": 2601.5,\n        \"max\": 9411.5,\n        \"num_unique_values\": 1528,\n        \"samples\": [\n          6798.0,\n          4901.0,\n          8531.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "qw = df.loc[crt].index\n",
        "print(qw)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2QsZF09snPRR",
        "outputId": "4a166918-0677-4cd2-83fc-1445052cebd3"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Int64Index([12246, 13002, 13118, 13757, 13991, 13992, 14138, 14912, 14913,\n",
            "            15151,\n",
            "            ...\n",
            "            27734, 27736, 27737, 27738, 27739, 27741, 27742, 27744, 27746,\n",
            "            27749],\n",
            "           dtype='int64', length=1889)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(qw,inplace=True)"
      ],
      "metadata": {
        "id": "WRqG6-ednWe4"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Y-OHDENOn0Ex",
        "outputId": "87b13c53-be71-4abf-8599-c9d1cb4ff60a"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   carat      cut color clarity  table  price     x     y     z  price_new\n",
              "0   0.23    Ideal     E     SI2   55.0    326  3.95  3.98  2.43      163.0\n",
              "1   0.21  Premium     E     SI1   61.0    326  3.89  3.84  2.31      163.0\n",
              "2   0.23     Good     E     VS1   65.0    327  4.05  4.07  2.31      163.5\n",
              "3   0.29  Premium     I     VS2   58.0    334  4.20  4.23  2.63      167.0\n",
              "4   0.31     Good     J     SI2   58.0    335  4.34  4.35  2.75      167.5"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e09f10ba-803b-4bf9-9901-d44b056392f7\" 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",
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              "    .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>carat</th>\n",
              "      <th>cut</th>\n",
              "      <th>color</th>\n",
              "      <th>clarity</th>\n",
              "      <th>table</th>\n",
              "      <th>price</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "      <th>price_new</th>\n",
              "    </tr>\n",
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              "      <td>Ideal</td>\n",
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              "      <td>55.0</td>\n",
              "      <td>326</td>\n",
              "      <td>3.95</td>\n",
              "      <td>3.98</td>\n",
              "      <td>2.43</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.21</td>\n",
              "      <td>Premium</td>\n",
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              "      <td>61.0</td>\n",
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              "      <td>3.89</td>\n",
              "      <td>3.84</td>\n",
              "      <td>2.31</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.23</td>\n",
              "      <td>Good</td>\n",
              "      <td>E</td>\n",
              "      <td>VS1</td>\n",
              "      <td>65.0</td>\n",
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              "      <td>4.05</td>\n",
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              "      <td>2.31</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.29</td>\n",
              "      <td>Premium</td>\n",
              "      <td>I</td>\n",
              "      <td>VS2</td>\n",
              "      <td>58.0</td>\n",
              "      <td>334</td>\n",
              "      <td>4.20</td>\n",
              "      <td>4.23</td>\n",
              "      <td>2.63</td>\n",
              "      <td>167.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.31</td>\n",
              "      <td>Good</td>\n",
              "      <td>J</td>\n",
              "      <td>SI2</td>\n",
              "      <td>58.0</td>\n",
              "      <td>335</td>\n",
              "      <td>4.34</td>\n",
              "      <td>4.35</td>\n",
              "      <td>2.75</td>\n",
              "      <td>167.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e09f10ba-803b-4bf9-9901-d44b056392f7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\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-e09f10ba-803b-4bf9-9901-d44b056392f7 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-e09f10ba-803b-4bf9-9901-d44b056392f7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-48c37c02-55be-449e-8ec7-b8bb6c411cdc')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"https://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\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-48c37c02-55be-449e-8ec7-b8bb6c411cdc 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": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 52051,\n  \"fields\": [\n    {\n      \"column\": \"carat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.40209634617484674,\n        \"min\": 0.2,\n        \"max\": 2.0,\n        \"num_unique_values\": 181,\n        \"samples\": [\n          0.78,\n          0.52,\n          1.91\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cut\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Premium\",\n          \"Fair\",\n          \"Good\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"E\",\n          \"I\",\n          \"G\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clarity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"SI1\",\n          \"VVS1\",\n          \"SI2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"table\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.223395841393578,\n        \"min\": 43.0,\n        \"max\": 79.0,\n        \"num_unique_values\": 127,\n        \"samples\": [\n          53.6,\n          61.5,\n          58.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3414,\n        \"min\": 326,\n        \"max\": 18818,\n        \"num_unique_values\": 10454,\n        \"samples\": [\n          10445,\n          8916,\n          6127\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0288945737910888,\n        \"min\": 0.0,\n        \"max\": 8.42,\n        \"num_unique_values\": 458,\n        \"samples\": [\n          7.31,\n          4.45,\n          7.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0543328719254583,\n        \"min\": 0.0,\n        \"max\": 58.9,\n        \"num_unique_values\": 462,\n        \"samples\": [\n          6.51,\n          4.31,\n          4.48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"z\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6485039859094903,\n        \"min\": 0.0,\n        \"max\": 31.8,\n        \"num_unique_values\": 309,\n        \"samples\": [\n          5.13,\n          2.73,\n          3.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_new\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1707.492676852149,\n        \"min\": 163.0,\n        \"max\": 9409.0,\n        \"num_unique_values\": 10454,\n        \"samples\": [\n          5222.5,\n          4458.0,\n          3063.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "5WB3Swwfn3lJ",
        "outputId": "247daea3-fcaa-4d91-b662-783895af3f7f"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       level_0  index  carat        cut color clarity  table  price     x  \\\n",
              "0            0      0   0.23      Ideal     E     SI2   55.0    326  3.95   \n",
              "1            1      1   0.21    Premium     E     SI1   61.0    326  3.89   \n",
              "2            2      2   0.23       Good     E     VS1   65.0    327  4.05   \n",
              "3            3      3   0.29    Premium     I     VS2   58.0    334  4.20   \n",
              "4            4      4   0.31       Good     J     SI2   58.0    335  4.34   \n",
              "...        ...    ...    ...        ...   ...     ...    ...    ...   ...   \n",
              "52046    52046  53935   0.72      Ideal     D     SI1   57.0   2757  5.75   \n",
              "52047    52047  53936   0.72       Good     D     SI1   55.0   2757  5.69   \n",
              "52048    52048  53937   0.70  Very Good     D     SI1   60.0   2757  5.66   \n",
              "52049    52049  53938   0.86    Premium     H     SI2   58.0   2757  6.15   \n",
              "52050    52050  53939   0.75      Ideal     D     SI2   55.0   2757  5.83   \n",
              "\n",
              "          y     z  price_new  \n",
              "0      3.98  2.43      163.0  \n",
              "1      3.84  2.31      163.0  \n",
              "2      4.07  2.31      163.5  \n",
              "3      4.23  2.63      167.0  \n",
              "4      4.35  2.75      167.5  \n",
              "...     ...   ...        ...  \n",
              "52046  5.76  3.50     1378.5  \n",
              "52047  5.75  3.61     1378.5  \n",
              "52048  5.68  3.56     1378.5  \n",
              "52049  6.12  3.74     1378.5  \n",
              "52050  5.87  3.64     1378.5  \n",
              "\n",
              "[52051 rows x 12 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>level_0</th>\n",
              "      <th>index</th>\n",
              "      <th>carat</th>\n",
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              "      <td>4.07</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>0.29</td>\n",
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              "      <td>52046</td>\n",
              "      <td>53935</td>\n",
              "      <td>0.72</td>\n",
              "      <td>Ideal</td>\n",
              "      <td>D</td>\n",
              "      <td>SI1</td>\n",
              "      <td>57.0</td>\n",
              "      <td>2757</td>\n",
              "      <td>5.75</td>\n",
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              "<p>52051 rows × 12 columns</p>\n",
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              "        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",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a1e656d3-581b-4bb1-b96a-dfed5bf51462 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_ce31d6a5-d1b6-4aff-b00f-2dea0d59271a\">\n",
              "    <style>\n",
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              "        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",
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              "\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",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"https://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "    </div>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 52051,\n  \"fields\": [\n    {\n      \"column\": \"level_0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15025,\n        \"min\": 0,\n        \"max\": 52050,\n        \"num_unique_values\": 52051,\n        \"samples\": [\n          22716,\n          508,\n          23787\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15843,\n        \"min\": 0,\n        \"max\": 53939,\n        \"num_unique_values\": 52051,\n        \"samples\": [\n          22853,\n          508,\n          24044\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"carat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.40209634617484674,\n        \"min\": 0.2,\n        \"max\": 2.0,\n        \"num_unique_values\": 181,\n        \"samples\": [\n          0.78,\n          0.52,\n          1.91\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cut\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Premium\",\n          \"Fair\",\n          \"Good\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"E\",\n          \"I\",\n          \"G\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clarity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"SI1\",\n          \"VVS1\",\n          \"SI2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"table\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.223395841393578,\n        \"min\": 43.0,\n        \"max\": 79.0,\n        \"num_unique_values\": 127,\n        \"samples\": [\n          53.6,\n          61.5,\n          58.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3414,\n        \"min\": 326,\n        \"max\": 18818,\n        \"num_unique_values\": 10454,\n        \"samples\": [\n          10445,\n          8916,\n          6127\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0288945737910888,\n        \"min\": 0.0,\n        \"max\": 8.42,\n        \"num_unique_values\": 458,\n        \"samples\": [\n          7.31,\n          4.45,\n          7.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0543328719254583,\n        \"min\": 0.0,\n        \"max\": 58.9,\n        \"num_unique_values\": 462,\n        \"samples\": [\n          6.51,\n          4.31,\n          4.48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"z\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6485039859094903,\n        \"min\": 0.0,\n        \"max\": 31.8,\n        \"num_unique_values\": 309,\n        \"samples\": [\n          5.13,\n          2.73,\n          3.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_new\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1707.492676852149,\n        \"min\": 163.0,\n        \"max\": 9409.0,\n        \"num_unique_values\": 10454,\n        \"samples\": [\n          5222.5,\n          4458.0,\n          3063.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop('level_0',axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "xQQauwBBoWlO"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "W7Jmoa1_otX_",
        "outputId": "43d48531-a517-4f93-8a33-8bf424d50146"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   index  carat      cut color clarity  table  price     x     y     z  \\\n",
              "0      0   0.23    Ideal     E     SI2   55.0    326  3.95  3.98  2.43   \n",
              "1      1   0.21  Premium     E     SI1   61.0    326  3.89  3.84  2.31   \n",
              "2      2   0.23     Good     E     VS1   65.0    327  4.05  4.07  2.31   \n",
              "3      3   0.29  Premium     I     VS2   58.0    334  4.20  4.23  2.63   \n",
              "4      4   0.31     Good     J     SI2   58.0    335  4.34  4.35  2.75   \n",
              "\n",
              "   price_new  \n",
              "0      163.0  \n",
              "1      163.0  \n",
              "2      163.5  \n",
              "3      167.0  \n",
              "4      167.5  "
            ],
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              "      <th>carat</th>\n",
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              "        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-21aed2c0-6681-422a-8540-b9fb0b800d75 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": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 52051,\n  \"fields\": [\n    {\n      \"column\": \"index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15843,\n        \"min\": 0,\n        \"max\": 53939,\n        \"num_unique_values\": 52051,\n        \"samples\": [\n          22853,\n          508,\n          24044\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"carat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.40209634617484674,\n        \"min\": 0.2,\n        \"max\": 2.0,\n        \"num_unique_values\": 181,\n        \"samples\": [\n          0.78,\n          0.52,\n          1.91\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cut\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Premium\",\n          \"Fair\",\n          \"Good\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"E\",\n          \"I\",\n          \"G\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clarity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"SI1\",\n          \"VVS1\",\n          \"SI2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"table\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.223395841393578,\n        \"min\": 43.0,\n        \"max\": 79.0,\n        \"num_unique_values\": 127,\n        \"samples\": [\n          53.6,\n          61.5,\n          58.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3414,\n        \"min\": 326,\n        \"max\": 18818,\n        \"num_unique_values\": 10454,\n        \"samples\": [\n          10445,\n          8916,\n          6127\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0288945737910888,\n        \"min\": 0.0,\n        \"max\": 8.42,\n        \"num_unique_values\": 458,\n        \"samples\": [\n          7.31,\n          4.45,\n          7.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0543328719254583,\n        \"min\": 0.0,\n        \"max\": 58.9,\n        \"num_unique_values\": 462,\n        \"samples\": [\n          6.51,\n          4.31,\n          4.48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"z\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6485039859094903,\n        \"min\": 0.0,\n        \"max\": 31.8,\n        \"num_unique_values\": 309,\n        \"samples\": [\n          5.13,\n          2.73,\n          3.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_new\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1707.492676852149,\n        \"min\": 163.0,\n        \"max\": 9409.0,\n        \"num_unique_values\": 10454,\n        \"samples\": [\n          5222.5,\n          4458.0,\n          3063.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.boxplot(df['carat'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "RewzBcaFowrT",
        "outputId": "f08fe47d-7ffa-4b5a-c6e0-212f87928ffd"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'whiskers': [<matplotlib.lines.Line2D at 0x7cc48bbc36d0>,\n",
              "  <matplotlib.lines.Line2D at 0x7cc48bbc2080>],\n",
              " 'caps': [<matplotlib.lines.Line2D at 0x7cc48bbc1570>,\n",
              "  <matplotlib.lines.Line2D at 0x7cc48bbc2d10>],\n",
              " 'boxes': [<matplotlib.lines.Line2D at 0x7cc48bbc3400>],\n",
              " 'medians': [<matplotlib.lines.Line2D at 0x7cc48bbc1030>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7cc48bbc27a0>],\n",
              " 'means': []}"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.boxplot(df['table'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "yO1LlIB-pW_D",
        "outputId": "48cd9e17-72dd-4cc2-ff19-e000cc807357"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'whiskers': [<matplotlib.lines.Line2D at 0x7cc48babd360>,\n",
              "  <matplotlib.lines.Line2D at 0x7cc48babe410>],\n",
              " 'caps': [<matplotlib.lines.Line2D at 0x7cc48babdf60>,\n",
              "  <matplotlib.lines.Line2D at 0x7cc48babf610>],\n",
              " 'boxes': [<matplotlib.lines.Line2D at 0x7cc48babeda0>],\n",
              " 'medians': [<matplotlib.lines.Line2D at 0x7cc48babf8e0>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7cc48babc400>],\n",
              " 'means': []}"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Q1 = np.percentile((df['table']),25,interpolation='midpoint')\n",
        "Q2 = np.percentile((df['table']),50,interpolation='midpoint')\n",
        "Q3 = np.percentile((df['table']),75,interpolation='midpoint')\n",
        "\n",
        "print(Q1)\n",
        "print(Q2)\n",
        "print(Q3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y0W0gOFMphMJ",
        "outputId": "28304368-6641-41e9-d527-bc152c927e6c"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "56.0\n",
            "57.0\n",
            "59.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "IQR = Q3-Q1\n",
        "IQR"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EWrsdlsRqaBm",
        "outputId": "0f02bbb9-b75b-4608-8428-e24d08e698ff"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.0"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "upperlimit=Q3+1.5*IQR\n",
        "lowerlimit=Q1-1.5*IQR\n",
        "print(upperlimit)\n",
        "print(lowerlimit)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tgp135c9qgMT",
        "outputId": "1021f3ae-ea64-4d0d-bd01-a7be61de45ac"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63.5\n",
            "51.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "outlier=[]\n",
        "for x in df['table']:\n",
        "  if ((x>upperlimit) or (x<lowerlimit)):\n",
        "    outlier.append(x)\n",
        "\n",
        "print(outlier)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uH-XD95Hq2Wr",
        "outputId": "e645349d-fff3-4bd9-9398-9adc921a5e90"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tbl=df['table']>upperlimit\n",
        "tbl"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "g6XdhrKMrmMj",
        "outputId": "0d78fa85-dcce-4c83-c7b9-40ffeb2a2fa7"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        False\n",
              "1        False\n",
              "2         True\n",
              "3        False\n",
              "4        False\n",
              "         ...  \n",
              "52046    False\n",
              "52047    False\n",
              "52048    False\n",
              "52049    False\n",
              "52050    False\n",
              "Name: table, Length: 52051, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tble=df['table']<lowerlimit\n",
        "tble"
      ],
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        },
        "id": "_HEiKAQOsuQF",
        "outputId": "bbc6c3da-2438-42a6-c741-78c1a0b11176"
      },
      "execution_count": 75,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "0        False\n",
              "1        False\n",
              "2        False\n",
              "3        False\n",
              "4        False\n",
              "         ...  \n",
              "52046    False\n",
              "52047    False\n",
              "52048    False\n",
              "52049    False\n",
              "52050    False\n",
              "Name: table, Length: 52051, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[tbl]"
      ],
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          "height": 423
        },
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        "outputId": "835e4932-5622-4fac-a6c1-32d109e53c61"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       index  carat        cut color clarity  table  price     x     y     z  \\\n",
              "2          2   0.23       Good     E     VS1   65.0    327  4.05  4.07  2.31   \n",
              "91        91   0.86       Fair     E     SI2   69.0   2757  6.45  6.33  3.52   \n",
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              "219      219   0.53  Very Good     D    VVS2   64.0   2782  5.34  5.37  3.08   \n",
              "227      227   0.84       Fair     G     SI1   67.0   2782  6.39  6.20  3.47   \n",
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              "51806  53695   0.75       Good     E     VS2   65.0   2717  5.85  5.80  3.48   \n",
              "51808  53697   0.70       Fair     H    VVS2   65.0   2717  5.52  5.45  3.54   \n",
              "51867  53756   0.84       Fair     G     VS1   64.0   2724  6.42  6.32  3.54   \n",
              "51868  53757   0.72       Fair     F     VS2   64.0   2724  6.06  5.97  3.34   \n",
              "51896  53785   0.89       Good     E     SI2   65.0   2728  6.00  5.95  3.84   \n",
              "\n",
              "       price_new  \n",
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              "91        1378.5  \n",
              "145       1383.5  \n",
              "219       1391.0  \n",
              "227       1391.0  \n",
              "...          ...  \n",
              "51806     1358.5  \n",
              "51808     1358.5  \n",
              "51867     1362.0  \n",
              "51868     1362.0  \n",
              "51896     1364.0  \n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
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            }
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t = df[tbl].index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LC2UPzkItNLG",
        "outputId": "5a4e33cd-b7e1-42a6-e62d-c55cad431366"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-82-b0250677f0d3>:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  t = df[tbl].index\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(t,inplace=True)"
      ],
      "metadata": {
        "id": "yLea706qtZqb"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "e = df[tble].index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uwMpuYQhtpMn",
        "outputId": "2aff54a0-7bc2-41c3-d996-cc6d62030603"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-81-160a6510a3cb>:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  e = df[tble].index\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(e,inplace=True)"
      ],
      "metadata": {
        "id": "fmwTfhP4t7Et"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "CfFB1BMxt_72",
        "outputId": "11d53e50-641f-4e8d-eceb-fe091c3bd8d4"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    index  carat        cut color clarity  table  price     x     y     z  \\\n",
              "0       0   0.23      Ideal     E     SI2   55.0    326  3.95  3.98  2.43   \n",
              "1       1   0.21    Premium     E     SI1   61.0    326  3.89  3.84  2.31   \n",
              "3       3   0.29    Premium     I     VS2   58.0    334  4.20  4.23  2.63   \n",
              "4       4   0.31       Good     J     SI2   58.0    335  4.34  4.35  2.75   \n",
              "5       5   0.24  Very Good     J    VVS2   57.0    336  3.94  3.96  2.48   \n",
              "6       6   0.24  Very Good     I    VVS1   57.0    336  3.95  3.98  2.47   \n",
              "7       7   0.26  Very Good     H     SI1   55.0    337  4.07  4.11  2.53   \n",
              "8       8   0.22       Fair     E     VS2   61.0    337  3.87  3.78  2.49   \n",
              "9       9   0.23  Very Good     H     VS1   61.0    338  4.00  4.05  2.39   \n",
              "10     10   0.30       Good     J     SI1   55.0    339  4.25  4.28  2.73   \n",
              "\n",
              "    price_new  \n",
              "0       163.0  \n",
              "1       163.0  \n",
              "3       167.0  \n",
              "4       167.5  \n",
              "5       168.0  \n",
              "6       168.0  \n",
              "7       168.5  \n",
              "8       168.5  \n",
              "9       169.0  \n",
              "10      169.5  "
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ddddc7eb-d2e2-4965-b616-8544e8fa6b3a');\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",
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              "      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",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 51482,\n  \"fields\": [\n    {\n      \"column\": \"index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15810,\n        \"min\": 0,\n        \"max\": 53939,\n        \"num_unique_values\": 51482,\n        \"samples\": [\n          47134,\n          18623,\n          17927\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"carat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4022616290805326,\n        \"min\": 0.2,\n        \"max\": 2.0,\n        \"num_unique_values\": 181,\n        \"samples\": [\n          0.96,\n          1.01,\n          1.65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cut\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Premium\",\n          \"Fair\",\n          \"Good\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"E\",\n          \"I\",\n          \"G\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clarity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"SI1\",\n          \"VS1\",\n          \"SI2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"table\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0701074813972284,\n        \"min\": 51.6,\n        \"max\": 63.5,\n        \"num_unique_values\": 107,\n        \"samples\": [\n          58.3,\n          55.3,\n          56.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3421,\n        \"min\": 326,\n        \"max\": 18818,\n        \"num_unique_values\": 10433,\n        \"samples\": [\n          5322,\n          1921,\n          3848\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0291297499551701,\n        \"min\": 0.0,\n        \"max\": 8.34,\n        \"num_unique_values\": 455,\n        \"samples\": [\n          7.15,\n          3.91,\n          8.14\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0552261369932827,\n        \"min\": 0.0,\n        \"max\": 58.9,\n        \"num_unique_values\": 461,\n        \"samples\": [\n          6.33,\n          4.16,\n          3.89\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"z\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6493353487105673,\n        \"min\": 0.0,\n        \"max\": 31.8,\n        \"num_unique_values\": 309,\n        \"samples\": [\n          3.02,\n          2.73,\n          3.35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_new\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1710.587119967548,\n        \"min\": 163.0,\n        \"max\": 9409.0,\n        \"num_unique_values\": 10433,\n        \"samples\": [\n          2661.0,\n          960.5,\n          1924.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.reset_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "jYVZHkOUuH8c",
        "outputId": "05c0feae-cf51-4d7a-9ae8-2d879b76c1df"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       level_0  index  carat        cut color clarity  table  price     x  \\\n",
              "0            0      0   0.23      Ideal     E     SI2   55.0    326  3.95   \n",
              "1            1      1   0.21    Premium     E     SI1   61.0    326  3.89   \n",
              "2            3      3   0.29    Premium     I     VS2   58.0    334  4.20   \n",
              "3            4      4   0.31       Good     J     SI2   58.0    335  4.34   \n",
              "4            5      5   0.24  Very Good     J    VVS2   57.0    336  3.94   \n",
              "...        ...    ...    ...        ...   ...     ...    ...    ...   ...   \n",
              "51477    52046  53935   0.72      Ideal     D     SI1   57.0   2757  5.75   \n",
              "51478    52047  53936   0.72       Good     D     SI1   55.0   2757  5.69   \n",
              "51479    52048  53937   0.70  Very Good     D     SI1   60.0   2757  5.66   \n",
              "51480    52049  53938   0.86    Premium     H     SI2   58.0   2757  6.15   \n",
              "51481    52050  53939   0.75      Ideal     D     SI2   55.0   2757  5.83   \n",
              "\n",
              "          y     z  price_new  \n",
              "0      3.98  2.43      163.0  \n",
              "1      3.84  2.31      163.0  \n",
              "2      4.23  2.63      167.0  \n",
              "3      4.35  2.75      167.5  \n",
              "4      3.96  2.48      168.0  \n",
              "...     ...   ...        ...  \n",
              "51477  5.76  3.50     1378.5  \n",
              "51478  5.75  3.61     1378.5  \n",
              "51479  5.68  3.56     1378.5  \n",
              "51480  6.12  3.74     1378.5  \n",
              "51481  5.87  3.64     1378.5  \n",
              "\n",
              "[51482 rows x 12 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>level_0</th>\n",
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              "      <th>51477</th>\n",
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              "      <td>Ideal</td>\n",
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              "      <td>5.75</td>\n",
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              "      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-e7814b3c-2f75-427d-9ece-c7d57032b00b 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-e7814b3c-2f75-427d-9ece-c7d57032b00b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2b2282fb-bd8e-4531-aeed-9f7772dbf2f6')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"https://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\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-2b2282fb-bd8e-4531-aeed-9f7772dbf2f6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 51482,\n  \"fields\": [\n    {\n      \"column\": \"level_0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14992,\n        \"min\": 0,\n        \"max\": 52050,\n        \"num_unique_values\": 51482,\n        \"samples\": [\n          45245,\n          18579,\n          17888\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15810,\n        \"min\": 0,\n        \"max\": 53939,\n        \"num_unique_values\": 51482,\n        \"samples\": [\n          47134,\n          18623,\n          17927\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"carat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4022616290805326,\n        \"min\": 0.2,\n        \"max\": 2.0,\n        \"num_unique_values\": 181,\n        \"samples\": [\n          0.96,\n          1.01,\n          1.65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cut\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Premium\",\n          \"Fair\",\n          \"Good\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"E\",\n          \"I\",\n          \"G\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clarity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"SI1\",\n          \"VS1\",\n          \"SI2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"table\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0701074813972284,\n        \"min\": 51.6,\n        \"max\": 63.5,\n        \"num_unique_values\": 107,\n        \"samples\": [\n          58.3,\n          55.3,\n          56.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3421,\n        \"min\": 326,\n        \"max\": 18818,\n        \"num_unique_values\": 10433,\n        \"samples\": [\n          5322,\n          1921,\n          3848\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0291297499551701,\n        \"min\": 0.0,\n        \"max\": 8.34,\n        \"num_unique_values\": 455,\n        \"samples\": [\n          7.15,\n          3.91,\n          8.14\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0552261369932827,\n        \"min\": 0.0,\n        \"max\": 58.9,\n        \"num_unique_values\": 461,\n        \"samples\": [\n          6.33,\n          4.16,\n          3.89\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"z\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6493353487105673,\n        \"min\": 0.0,\n        \"max\": 31.8,\n        \"num_unique_values\": 309,\n        \"samples\": [\n          3.02,\n          2.73,\n          3.35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_new\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1710.587119967548,\n        \"min\": 163.0,\n        \"max\": 9409.0,\n        \"num_unique_values\": 10433,\n        \"samples\": [\n          2661.0,\n          960.5,\n          1924.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.boxplot(df['table'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "zbCzQdAbuOu8",
        "outputId": "6e91dae9-0732-45fc-9dab-d4ea0fdb27b7"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'whiskers': [<matplotlib.lines.Line2D at 0x7cc48b9d23e0>,\n",
              "  <matplotlib.lines.Line2D at 0x7cc48b9d2680>],\n",
              " 'caps': [<matplotlib.lines.Line2D at 0x7cc48b9d2920>,\n",
              "  <matplotlib.lines.Line2D at 0x7cc48b9d2bc0>],\n",
              " 'boxes': [<matplotlib.lines.Line2D at 0x7cc48b9d1f60>],\n",
              " 'medians': [<matplotlib.lines.Line2D at 0x7cc48b9d2e60>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7cc48b9d3100>],\n",
              " 'means': []}"
            ]
          },
          "metadata": {},
          "execution_count": 86
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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577778uEPf/iUDA8ADD4VxccDDzxw0v0TJ070iaYAwEn5bhcAoCjxAQAUJT4AgKLEBwBQlPgAAIoSHwBAUeIDAChKfAAARYkPAKAo8QEAFCU+AICixAcAUJT4AACKEh8AQFHiAwAoSnwAAEWJDwCgKPEBABQlPgCAosQHAFCU+AAAiqoe6AGAU6PqN8cyfcxZqT24K2n374yTqT24K9PHnJWq3xwb6FHgjCA+YIg69/C+bP30HyQ//nTy44Ge5vTWkmTrp/8g2w/vSzJ7oMeBIU98wBB17A/GZcb/O5zvfOc7aWluHuhxTmvbd+zIokWL8q0PjxvoUeCMID5giOquPjc/PXA8R0dNShouH+hxTmtHDxzPTw8cT3f1uQM9CpwRvBEMABQlPgCAosQHAFCU+AAAihIfAEBR4gMAKEp8AABFiQ8AoCjxAQAUJT4AgKLEBwBQlPgAAIoSHwBAUeIDAChKfAAARYkPAKAo8QEAFCU+AICixAcAUJT4AACKEh8AQFEVxccdd9yRqqqqXo/m5uZexzzxxBP5wAc+kPPOOy8jR47MH//xH+fo0aP9OjQAMHhVV3rC1KlTs3HjxtcXqH59iSeeeCIf+tCHsnz58vzbv/1bqqur83//93856ywXWACAV1UcH9XV1RkzZswJ9y1btixLly7NF7/4xZ5tkydPfufTAQBDTsXx0dbWloaGhpx77rl53/vel9bW1owbNy6//OUv8+STT2bRokWZPXt29uzZk+bm5nz1q1/N3Llz33S9rq6udHV19Tzv7Ox8Z78J0MuRI0eSJFu3bh3gSV539OjR7N27N+PHj09tbe1Aj9Nj+/btAz0CnFEqio9Zs2Zl7dq1mTx5cl544YWsWLEiV111VbZt25ann346yav3hXzta1/L5Zdfnm9/+9v5kz/5k2zbti0TJ0484Zqtra1ZsWJF338ToJcdO3YkSW688cYBnmTwGDFixECPAGeEqu7u7u53evLBgwfT1NSUr3/962lpacmcOXOyfPnyrFy5sueYSy+9NNddd11aW1tPuMaJrnw0Njamo6MjI0eOfKejwRnvV7/6VdavX5/m5uYMHz58oMdJ8uoVhsWLF+f+++9PS0vLQI/Ty4gRI970H0nAW+vs7ExdXd3bev2u+G2X3zVq1KhMmjQpu3fvzgc+8IEkyZQpU3od09LSkn379r3pGjU1NampqenLGMAJXHjhhfnUpz410GOcUEtLS2bMmDHQYwADpE9/hnL48OHs2bMnY8eOzfjx49PQ0JCdO3f2OmbXrl1pamrq05AAwNBR0ZWPW2+9NfPnz09TU1Pa29tz++23Z9iwYVm4cGGqqqry+c9/Prfffnsuu+yyXH755bnvvvuyY8eOfPe73z1V8wMAg0xF8fHcc89l4cKFefHFF1NfX5+5c+dm06ZNqa+vT5LccsstOXbsWJYtW5aXXnopl112WR555JG8+93vPiXDAwCDT59uOD0VKrlhBRhctm7dmpkzZ2bLli3u+YAhppLXbx89CgAUJT4AgKLEBwBQlPgAAIoSHwBAUeIDAChKfAAARYkPAKAo8QEAFCU+AICixAcAUJT4AACKEh8AQFHiAwAoSnwAAEWJDwCgKPEBABQlPgCAosQHAFCU+AAAihIfAEBR4gMAKEp8AABFiQ8AoCjxAQAUJT4AgKLEBwBQlPgAAIoSHwBAUeIDAChKfAAARYkPAKAo8QEAFCU+AICixAcAUJT4AACKEh8AQFHiAwAoSnwAAEWJDwCgKPEBABQlPgCAosQHAFCU+AAAihIfAEBRFcXHHXfckaqqql6P5ubmNxzX3d2defPmpaqqKuvXr++vWQGAIaC60hOmTp2ajRs3vr5A9RuXWLVqVaqqqvo2GQAwJFUcH9XV1RkzZsyb7v/Zz36Wu+66K5s3b87YsWP7NBwAMPRUfM9HW1tbGhoacvHFF2fRokXZt29fz74jR47kr//6r3P33XefNFAAgDNXRVc+Zs2albVr12by5Ml54YUXsmLFilx11VXZtm1bRowYkWXLlmX27Nn56Ec/+rbX7OrqSldXV8/zzs7OSkYCAAaZiuJj3rx5PT9feumlmTVrVpqamvLggw+mvr4+P/jBD/LTn/60ogFaW1uzYsWKis4BAAavPv2p7ahRozJp0qTs3r07P/jBD7Jnz56MGjUq1dXVPTei/vmf/3muvvrqN11j+fLl6ejo6Hns37+/LyMBAKe5im84/V2HDx/Onj17smTJkixYsCCf+tSneu2fNm1a/uVf/iXz589/0zVqampSU1PTlzEAgEGkovi49dZbM3/+/DQ1NaW9vT233357hg0bloULF6a+vv6EN5mOGzcuEyZM6LeBAYDBraL4eO6557Jw4cK8+OKLqa+vz9y5c7Np06bU19efqvkAgCGmovh44IEHKlq8u7u7ouMBgKHPd7sAAEWJDwCgKPEBABQlPgCAosQHAFCU+AAAihIfAEBR4gMAKEp8AABFiQ8AoCjxAQAUJT4AgKLEBwBQlPgAAIoSHwBAUeIDAChKfAAARYkPAKAo8QEAFCU+AICixAcAUJT4AACKEh8AQFHiAwAoSnwAAEWJDwCgKPEBABQlPgCAosQHAFCU+AAAihIfAEBR4gMAKEp8AABFiQ8AoCjxAQAUJT4AgKLEBwBQlPgAAIoSHwBAUeIDAChKfAAARYkPAKAo8QEAFCU+AICixAcAUJT4AACKqig+7rjjjlRVVfV6NDc3J0leeuml3HzzzZk8eXJqa2szbty4LF26NB0dHadkcABgcKqu9ISpU6dm48aNry9Q/eoS7e3taW9vz9e+9rVMmTIlzz77bD7zmc+kvb093/3ud/tvYgBgUKs4PqqrqzNmzJg3bH/Pe96T733vez3P3/3ud+erX/1qFi9enN/85jc9kQIAnNkqvuejra0tDQ0Nufjii7No0aLs27fvTY/t6OjIyJEjTxoeXV1d6ezs7PUAAIauiuJj1qxZWbt2bb7//e9n9erVeeaZZ3LVVVfl0KFDbzj2V7/6Vb7yla/kpptuOumara2tqaur63k0NjZW9hsAAINKVXd3d/c7PfngwYNpamrK17/+9Xzyk5/s2d7Z2Zk//dM/zQUXXJANGzbk7LPPftM1urq60tXV1evcxsbGnqsmwNCxdevWzJw5M1u2bMmMGTMGehygH3V2dqauru5tvX736UaMUaNGZdKkSdm9e3fPtkOHDuVDH/pQRowYkXXr1p00PJKkpqYmNTU1fRkDABhE+vQ5H4cPH86ePXsyduzYJK9Wzwc/+MGcc8452bBhQ84999x+GRIAGDoqio9bb701jz76aPbu3ZvHH388H//4xzNs2LAsXLiwJzxeeeWVfOtb30pnZ2cOHDiQAwcO5Le//e2pmh8AGGQqetvlueeey8KFC/Piiy+mvr4+c+fOzaZNm1JfX58f/ehHefLJJ5Mkl1xySa/znnnmmYwfP77fhgYABq+K4uOBBx54031XX311+nDvKgBwhvDdLgBAUeIDAChKfAAARfnCFeAtHTlyJDt27OjzOtu3b+/13/7Q3Nyc4cOH99t6wKknPoC3tGPHjsycObPf1lu8eHG/reXTUmHwER/AW2pubs6WLVv6vM7Ro0ezd+/ejB8/PrW1tf0w2auzAYNLn77b5VSo5LPhAYDTQyWv3244BQCKEh8AQFHiAwAoSnwAAEWJDwCgKPEBABQlPgCAosQHAFCU+AAAihIfAEBR4gMAKEp8AABFiQ8AoKjqgR7g9732JbudnZ0DPAkA8Ha99rr92uv4yZx28XHo0KEkSWNj4wBPAgBU6tChQ6mrqzvpMVXdbydRCjp+/Hja29szYsSIVFVVDfQ4QD/q7OxMY2Nj9u/fn5EjRw70OEA/6u7uzqFDh9LQ0JCzzjr5XR2nXXwAQ1dnZ2fq6urS0dEhPuAM5oZTAKAo8QEAFCU+gGJqampy++23p6amZqBHAQaQez4AgKJc+QAAihIfAEBR4gMAKEp8AABFiQ/glPvxj3+c+fPnp6GhIVVVVVm/fv1AjwQMIPEBnHKvvPJKLrvsstx9990DPQpwGjjtvlgOGHrmzZuXefPmDfQYwGnClQ8AoCjxAQAUJT4AgKLEBwBQlPgAAIry1y7AKXf48OHs3r275/kzzzyTn/3sZ7ngggsybty4AZwMGAi+1RY45X70ox/l/e9//xu233DDDVm7dm35gYABJT4AgKLc8wEAFCU+AICixAcAUJT4AACKEh8AQFHiAwAoSnwAAEWJDwCgKPEBABQlPgCAosQHAFCU+AAAivr/4xlCGT7NNrwAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}