data_preprocessing_practice_3.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
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      "source": [
        "# 1\n",
        "ds = pd.read_csv('/content/drive/MyDrive/Datasets/titanic.csv')\n",
        "ds.head(10)"
      ],
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              "   Survived  Pclass                                               Name  \\\n",
              "0         0       3                             Mr. Owen Harris Braund   \n",
              "1         1       1  Mrs. John Bradley (Florence Briggs Thayer) Cum...   \n",
              "2         1       3                              Miss. Laina Heikkinen   \n",
              "3         1       1        Mrs. Jacques Heath (Lily May Peel) Futrelle   \n",
              "4         0       3                            Mr. William Henry Allen   \n",
              "5         0       3                                    Mr. James Moran   \n",
              "6         0       1                             Mr. Timothy J McCarthy   \n",
              "7         0       3                      Master. Gosta Leonard Palsson   \n",
              "8         1       3   Mrs. Oscar W (Elisabeth Vilhelmina Berg) Johnson   \n",
              "9         1       2                 Mrs. Nicholas (Adele Achem) Nasser   \n",
              "\n",
              "      Sex   Age  Siblings/Spouses Aboard  Parents/Children Aboard     Fare  \n",
              "0    male  22.0                        1                        0   7.2500  \n",
              "1  female  38.0                        1                        0  71.2833  \n",
              "2  female  26.0                        0                        0   7.9250  \n",
              "3  female  35.0                        1                        0  53.1000  \n",
              "4    male  35.0                        0                        0   8.0500  \n",
              "5    male  27.0                        0                        0   8.4583  \n",
              "6    male  54.0                        0                        0  51.8625  \n",
              "7    male   2.0                        3                        1  21.0750  \n",
              "8  female  27.0                        0                        2  11.1333  \n",
              "9  female  14.0                        1                        0  30.0708  "
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              "      <td>Mrs. John Bradley (Florence Briggs Thayer) Cum...</td>\n",
              "      <td>female</td>\n",
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              "      <td>Mrs. Jacques Heath (Lily May Peel) Futrelle</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
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              "      <td>53.1000</td>\n",
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              "      <th>4</th>\n",
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              "      <td>Mr. William Henry Allen</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
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              "      <td>8.0500</td>\n",
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              "      <td>Mr. James Moran</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
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              "      <td>8.4583</td>\n",
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              "      <td>1</td>\n",
              "      <td>Mr. Timothy J McCarthy</td>\n",
              "      <td>male</td>\n",
              "      <td>54.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>51.8625</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
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              "      <td>Master. Gosta Leonard Palsson</td>\n",
              "      <td>male</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>21.0750</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Mrs. Oscar W (Elisabeth Vilhelmina Berg) Johnson</td>\n",
              "      <td>female</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>11.1333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
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              "      <td>Mrs. Nicholas (Adele Achem) Nasser</td>\n",
              "      <td>female</td>\n",
              "      <td>14.0</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ds",
              "summary": "{\n  \"name\": \"ds\",\n  \"rows\": 887,\n  \"fields\": [\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 887,\n        \"samples\": [\n          \"Mr. Adolphe Saalfeld\",\n          \"Mr. Joseph Philippe Lemercier Laroche\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.12190840546256,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          33.0,\n          9.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Siblings/Spouses Aboard\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parents/Children Aboard\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.78204040017391,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2\n",
        "ds['Age'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PbEBidGG8qh4",
        "outputId": "6feaf33b-629b-460f-f8f2-0d1ac05a6fd6"
      },
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29.471443066516347"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3\n",
        "ds = pd.read_csv('/content/drive/MyDrive/Datasets/WHO-COVID-19-global-data.csv')\n",
        "ds[ds['Date_reported'].apply(lambda date: date.split('-')[0] == '2022')]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "_EM71tCI8u9S",
        "outputId": "6145b5e9-2ca6-413c-e618-1ae92e18dcf0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Date_reported Country_code      Country WHO_region  New_cases  \\\n",
              "104      2022-01-02           AF  Afghanistan       EMRO      210.0   \n",
              "105      2022-01-09           AF  Afghanistan       EMRO      233.0   \n",
              "106      2022-01-16           AF  Afghanistan       EMRO      333.0   \n",
              "107      2022-01-23           AF  Afghanistan       EMRO      870.0   \n",
              "108      2022-01-30           AF  Afghanistan       EMRO     2118.0   \n",
              "...             ...          ...          ...        ...        ...   \n",
              "53448    2022-11-27           ZW     Zimbabwe       AFRO      311.0   \n",
              "53449    2022-12-04           ZW     Zimbabwe       AFRO      396.0   \n",
              "53450    2022-12-11           ZW     Zimbabwe       AFRO        6.0   \n",
              "53451    2022-12-18           ZW     Zimbabwe       AFRO      203.0   \n",
              "53452    2022-12-25           ZW     Zimbabwe       AFRO      385.0   \n",
              "\n",
              "       Cumulative_cases  New_deaths  Cumulative_deaths  \n",
              "104              158112         4.0               7356  \n",
              "105              158345        13.0               7369  \n",
              "106              158678         9.0               7378  \n",
              "107              159548        12.0               7390  \n",
              "108              161666        17.0               7407  \n",
              "...                 ...         ...                ...  \n",
              "53448            258954         5.0               5616  \n",
              "53449            259350         6.0               5622  \n",
              "53450            259356         NaN               5622  \n",
              "53451            259559         3.0               5625  \n",
              "53452            259944        10.0               5635  \n",
              "\n",
              "[12480 rows x 8 columns]"
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              "      <th></th>\n",
              "      <th>Date_reported</th>\n",
              "      <th>Country_code</th>\n",
              "      <th>Country</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>2022-01-02</td>\n",
              "      <td>AF</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>EMRO</td>\n",
              "      <td>210.0</td>\n",
              "      <td>158112</td>\n",
              "      <td>4.0</td>\n",
              "      <td>7356</td>\n",
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              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>2022-01-09</td>\n",
              "      <td>AF</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>EMRO</td>\n",
              "      <td>233.0</td>\n",
              "      <td>158345</td>\n",
              "      <td>13.0</td>\n",
              "      <td>7369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>2022-01-16</td>\n",
              "      <td>AF</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>EMRO</td>\n",
              "      <td>333.0</td>\n",
              "      <td>158678</td>\n",
              "      <td>9.0</td>\n",
              "      <td>7378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>2022-01-23</td>\n",
              "      <td>AF</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>EMRO</td>\n",
              "      <td>870.0</td>\n",
              "      <td>159548</td>\n",
              "      <td>12.0</td>\n",
              "      <td>7390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>2022-01-30</td>\n",
              "      <td>AF</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>EMRO</td>\n",
              "      <td>2118.0</td>\n",
              "      <td>161666</td>\n",
              "      <td>17.0</td>\n",
              "      <td>7407</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>53448</th>\n",
              "      <td>2022-11-27</td>\n",
              "      <td>ZW</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>AFRO</td>\n",
              "      <td>311.0</td>\n",
              "      <td>258954</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53449</th>\n",
              "      <td>2022-12-04</td>\n",
              "      <td>ZW</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>AFRO</td>\n",
              "      <td>396.0</td>\n",
              "      <td>259350</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53450</th>\n",
              "      <td>2022-12-11</td>\n",
              "      <td>ZW</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>AFRO</td>\n",
              "      <td>6.0</td>\n",
              "      <td>259356</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53451</th>\n",
              "      <td>2022-12-18</td>\n",
              "      <td>ZW</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>AFRO</td>\n",
              "      <td>203.0</td>\n",
              "      <td>259559</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53452</th>\n",
              "      <td>2022-12-25</td>\n",
              "      <td>ZW</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>AFRO</td>\n",
              "      <td>385.0</td>\n",
              "      <td>259944</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5635</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12480 rows × 8 columns</p>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    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",
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              "    20% {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4\n",
        "header = ['Type', 'Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Magnesium',\n",
        "'Total phenols', 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins',\n",
        "'Color intensity', 'Hue', 'OD280/OD315 of diluted wines', 'Proline']\n",
        "\n",
        "ds = pd.read_csv('/content/drive/MyDrive/Datasets/wine.data', header=None)\n",
        "ds.columns = header\n",
        "ds['Alcohol amount'] = ['High' if x >= ds['Alcohol'].median() else 'Low' for x in ds['Alcohol']]\n",
        "ds['Alcohol amount'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A8MfxvA8-IvW",
        "outputId": "2f2564eb-b066-40df-8ce0-e8b9e9deb17a"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Alcohol amount\n",
              "High    91\n",
              "Low     87\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5\n",
        "ds = pd.read_csv('/content/drive/MyDrive/Datasets/titanic.csv')\n",
        "ds['Survived'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NazJ_WmiFRDC",
        "outputId": "7fe4838d-64e8-4548-8bdc-71feb9e105a1"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived\n",
              "0    545\n",
              "1    342\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 6\n",
        "ds = pd.read_csv('/content/drive/MyDrive/Datasets/WHO-COVID-19-global-data.csv')\n",
        "ds.groupby('Country_code').sum()['Cumulative_cases']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b6JWQNorFW2O",
        "outputId": "7ece9fd9-1646-44f0-de01-b81028203b8a"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Country_code\n",
              "AD      6230178\n",
              "AE    153820510\n",
              "AF     31344178\n",
              "AG      1132666\n",
              "AI       447284\n",
              "        ...    \n",
              "YE      1803357\n",
              "YT      5857424\n",
              "ZA    604442817\n",
              "ZM     47731294\n",
              "ZW     35142881\n",
              "Name: Cumulative_cases, Length: 239, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 7 Merge with another dataset\n",
        "cabins = pd.Series(np.random.randint(1, 5, 887))\n",
        "ds = pd.read_csv('/content/drive/MyDrive/Datasets/titanic.csv')"
      ],
      "metadata": {
        "id": "l-61lSlaIYVp"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 8\n",
        "ds = pd.read_csv('/content/drive/MyDrive/Datasets/titanic.csv')\n",
        "ds.pivot_table(values='Age', index='Survived', aggfunc='mean')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "-0eS1vXhMbuE",
        "outputId": "d9ffc83f-ebc2-44d5-c3c1-ed4f29238b76"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Age\n",
              "Survived           \n",
              "0         30.138532\n",
              "1         28.408392"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-72a1ede5-57ea-44fd-a2f4-83267b83eb33\" class=\"colab-df-container\">\n",
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              "        vertical-align: middle;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>30.138532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>28.408392</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "\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",
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              "\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-72a1ede5-57ea-44fd-a2f4-83267b83eb33 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-72a1ede5-57ea-44fd-a2f4-83267b83eb33');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1ef7e61f-e356-406f-b912-5530ea4b9332')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1ef7e61f-e356-406f-b912-5530ea4b9332 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"ds\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2233939365727873,\n        \"min\": 28.408391812865496,\n        \"max\": 30.13853211009174,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          28.408391812865496,\n          30.13853211009174\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 9\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "header = ['Type', 'Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Magnesium',\n",
        "'Total phenols', 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins',\n",
        "'Color intensity', 'Hue', 'OD280/OD315 of diluted wines', 'Proline']\n",
        "\n",
        "ds = pd.read_csv('/content/drive/MyDrive/Datasets/wine.data', header=None)\n",
        "ds.columns = header\n",
        "\n",
        "ds['Wine quality'] = ds['Proline'] >= 700\n",
        "model = LogisticRegression()\n",
        "model.fit(ds[['Alcohol', 'Malic acid', 'Ash']], ds['Wine quality'])\n",
        "print(f'Model coefficients: {model.coef_}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sft3lAdKOrqT",
        "outputId": "5dd50a9c-cb4e-496d-d048-fc3a6a76b5ba"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model coefficients: [[ 1.79799631 -0.4544988   0.392962  ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 10\n",
        "ds = pd.read_csv('/content/drive/MyDrive/Datasets/titanic.csv')\n",
        "\n",
        "fare_min = ds['Fare'].min()\n",
        "fare_max = ds['Fare'].max()\n",
        "\n",
        "ds['Fare_normalized'] = (ds['Fare'] - fare_min) / (fare_max - fare_min)\n",
        "print(ds[['Fare', 'Fare_normalized']])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DD0eNBEySY2D",
        "outputId": "ca589a63-e51d-4188-c69a-79d1a8e81f21"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        Fare  Fare_normalized\n",
            "0     7.2500         0.014151\n",
            "1    71.2833         0.139136\n",
            "2     7.9250         0.015469\n",
            "3    53.1000         0.103644\n",
            "4     8.0500         0.015713\n",
            "..       ...              ...\n",
            "882  13.0000         0.025374\n",
            "883  30.0000         0.058556\n",
            "884  23.4500         0.045771\n",
            "885  30.0000         0.058556\n",
            "886   7.7500         0.015127\n",
            "\n",
            "[887 rows x 2 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 11\n",
        "ds = pd.read_csv('/content/drive/MyDrive/Datasets/WHO-COVID-19-global-data.csv')\n",
        "ds['Date_reported'] = pd.to_datetime(ds['Date_reported'])\n",
        "ds['Date_reported'].resample('M').sum()['New_cases']"
      ],
      "metadata": {
        "id": "vC5rPU0TTFYb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 13\n",
        "ds = pd.read_csv('/content/drive/MyDrive/Datasets/winequality-red.csv', sep=';')\n",
        "Q1 = ds['sulphates'].quantile(0.25)\n",
        "Q3 = ds['sulphates'].quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "outliers = ds[(ds['sulphates'] < (Q1 - 1.5 * IQR)) | (ds['sulphates'] > (Q3 + 1.5 * IQR))]\n",
        "outliers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "kGgaMOIXvDN5",
        "outputId": "348c57ef-0086-484a-d251-9333412240a3"
      },
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "13              7.8             0.610         0.29             1.6      0.114   \n",
              "17              8.1             0.560         0.28             1.7      0.368   \n",
              "19              7.9             0.320         0.51             1.8      0.341   \n",
              "43              8.1             0.660         0.22             2.2      0.069   \n",
              "79              8.3             0.625         0.20             1.5      0.080   \n",
              "81              7.8             0.430         0.70             1.9      0.464   \n",
              "83              7.3             0.670         0.26             1.8      0.401   \n",
              "86              8.6             0.490         0.28             1.9      0.110   \n",
              "88              9.3             0.390         0.44             2.1      0.107   \n",
              "91              8.6             0.490         0.28             1.9      0.110   \n",
              "92              8.6             0.490         0.29             2.0      0.110   \n",
              "106             7.8             0.410         0.68             1.7      0.467   \n",
              "151             9.2             0.520         1.00             3.4      0.610   \n",
              "161             7.6             0.680         0.02             1.3      0.072   \n",
              "169             7.5             0.705         0.24             1.8      0.360   \n",
              "181             8.9             0.610         0.49             2.0      0.270   \n",
              "201             8.8             0.370         0.48             2.1      0.097   \n",
              "226             8.9             0.590         0.50             2.0      0.337   \n",
              "240             8.9             0.635         0.37             1.7      0.263   \n",
              "258             7.7             0.410         0.76             1.8      0.611   \n",
              "281             7.7             0.270         0.68             3.5      0.358   \n",
              "338            12.4             0.490         0.58             3.0      0.103   \n",
              "339            12.5             0.280         0.54             2.3      0.082   \n",
              "340            12.2             0.340         0.50             2.4      0.066   \n",
              "369             9.4             0.270         0.53             2.4      0.074   \n",
              "372             9.1             0.280         0.48             1.8      0.067   \n",
              "376            11.5             0.450         0.50             3.0      0.078   \n",
              "377             9.4             0.270         0.53             2.4      0.074   \n",
              "415             8.6             0.725         0.24             6.6      0.117   \n",
              "451             8.4             0.370         0.53             1.8      0.413   \n",
              "477            10.4             0.240         0.49             1.8      0.075   \n",
              "482            10.6             0.360         0.59             2.2      0.152   \n",
              "483            10.6             0.360         0.60             2.2      0.152   \n",
              "503            10.5             0.260         0.47             1.9      0.078   \n",
              "504            10.5             0.240         0.42             1.8      0.077   \n",
              "506            10.4             0.240         0.46             1.8      0.075   \n",
              "515             8.5             0.655         0.49             6.1      0.122   \n",
              "586            11.1             0.310         0.49             2.7      0.094   \n",
              "614             9.2             0.755         0.18             2.2      0.148   \n",
              "639             8.9             0.290         0.35             1.9      0.067   \n",
              "689             8.1             0.380         0.48             1.8      0.157   \n",
              "692             8.6             0.490         0.51             2.0      0.422   \n",
              "723             7.1             0.310         0.30             2.2      0.053   \n",
              "754             7.8             0.480         0.68             1.7      0.415   \n",
              "795            10.8             0.890         0.30             2.6      0.132   \n",
              "852             8.0             0.420         0.32             2.5      0.080   \n",
              "1051            8.5             0.460         0.59             1.4      0.414   \n",
              "1158            6.7             0.410         0.43             2.8      0.076   \n",
              "1165            8.5             0.440         0.50             1.9      0.369   \n",
              "1260            8.6             0.635         0.68             1.8      0.403   \n",
              "1288            7.0             0.600         0.30             4.5      0.068   \n",
              "1289            7.0             0.600         0.30             4.5      0.068   \n",
              "1319            9.1             0.760         0.68             1.7      0.414   \n",
              "1367            6.9             0.540         0.30             2.2      0.088   \n",
              "1370            8.7             0.780         0.51             1.7      0.415   \n",
              "1371            7.5             0.580         0.56             3.1      0.153   \n",
              "1372            8.7             0.780         0.51             1.7      0.415   \n",
              "1403            7.2             0.330         0.33             1.7      0.061   \n",
              "1408            8.1             0.290         0.36             2.2      0.048   \n",
              "\n",
              "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "13                    9.0                  29.0  0.99740  3.26       1.56   \n",
              "17                   16.0                  56.0  0.99680  3.11       1.28   \n",
              "19                   17.0                  56.0  0.99690  3.04       1.08   \n",
              "43                    9.0                  23.0  0.99680  3.30       1.20   \n",
              "79                   27.0                 119.0  0.99720  3.16       1.12   \n",
              "81                   22.0                  67.0  0.99740  3.13       1.28   \n",
              "83                   16.0                  51.0  0.99690  3.16       1.14   \n",
              "86                   20.0                 136.0  0.99720  2.93       1.95   \n",
              "88                   34.0                 125.0  0.99780  3.14       1.22   \n",
              "91                   20.0                 136.0  0.99720  2.93       1.95   \n",
              "92                   19.0                 133.0  0.99720  2.93       1.98   \n",
              "106                  18.0                  69.0  0.99730  3.08       1.31   \n",
              "151                  32.0                  69.0  0.99960  2.74       2.00   \n",
              "161                   9.0                  20.0  0.99650  3.17       1.08   \n",
              "169                  15.0                  63.0  0.99640  3.00       1.59   \n",
              "181                  23.0                 110.0  0.99720  3.12       1.02   \n",
              "201                  39.0                 145.0  0.99750  3.04       1.03   \n",
              "226                  27.0                  81.0  0.99640  3.04       1.61   \n",
              "240                   5.0                  62.0  0.99710  3.00       1.09   \n",
              "258                   8.0                  45.0  0.99680  3.06       1.26   \n",
              "281                   5.0                  10.0  0.99720  3.25       1.08   \n",
              "338                  28.0                  99.0  1.00080  3.16       1.00   \n",
              "339                  12.0                  29.0  0.99970  3.11       1.36   \n",
              "340                  10.0                  21.0  1.00000  3.12       1.18   \n",
              "369                   6.0                  18.0  0.99620  3.20       1.13   \n",
              "372                  26.0                  46.0  0.99670  3.32       1.04   \n",
              "376                  19.0                  47.0  1.00030  3.26       1.11   \n",
              "377                   6.0                  18.0  0.99620  3.20       1.13   \n",
              "415                  31.0                 134.0  1.00140  3.32       1.07   \n",
              "451                   9.0                  26.0  0.99790  3.06       1.06   \n",
              "477                   6.0                  20.0  0.99770  3.18       1.06   \n",
              "482                   6.0                  18.0  0.99860  3.04       1.05   \n",
              "483                   7.0                  18.0  0.99860  3.04       1.06   \n",
              "503                   6.0                  24.0  0.99760  3.18       1.04   \n",
              "504                   6.0                  22.0  0.99760  3.21       1.05   \n",
              "506                   6.0                  21.0  0.99760  3.25       1.02   \n",
              "515                  34.0                 151.0  1.00100  3.31       1.14   \n",
              "586                  16.0                  47.0  0.99860  3.12       1.02   \n",
              "614                  10.0                 103.0  0.99690  2.87       1.36   \n",
              "639                  25.0                  57.0  0.99700  3.18       1.36   \n",
              "689                   5.0                  17.0  0.99760  3.30       1.05   \n",
              "692                  16.0                  62.0  0.99790  3.03       1.17   \n",
              "723                  36.0                 127.0  0.99650  2.94       1.62   \n",
              "754                  14.0                  32.0  0.99656  3.09       1.06   \n",
              "795                   7.0                  60.0  0.99786  2.99       1.18   \n",
              "852                  26.0                 122.0  0.99801  3.22       1.07   \n",
              "1051                 16.0                  45.0  0.99702  3.03       1.34   \n",
              "1158                 22.0                  54.0  0.99572  3.42       1.16   \n",
              "1165                 15.0                  38.0  0.99634  3.01       1.10   \n",
              "1260                 19.0                  56.0  0.99632  3.02       1.15   \n",
              "1288                 20.0                 110.0  0.99914  3.30       1.17   \n",
              "1289                 20.0                 110.0  0.99914  3.30       1.17   \n",
              "1319                 18.0                  64.0  0.99652  2.90       1.33   \n",
              "1367                  9.0                 105.0  0.99725  3.25       1.18   \n",
              "1370                 12.0                  66.0  0.99623  3.00       1.17   \n",
              "1371                  5.0                  14.0  0.99476  3.21       1.03   \n",
              "1372                 12.0                  66.0  0.99623  3.00       1.17   \n",
              "1403                  3.0                  13.0  0.99600  3.23       1.10   \n",
              "1408                 35.0                  53.0  0.99500  3.27       1.01   \n",
              "\n",
              "      alcohol  quality  \n",
              "13        9.1        5  \n",
              "17        9.3        5  \n",
              "19        9.2        6  \n",
              "43       10.3        5  \n",
              "79        9.1        4  \n",
              "81        9.4        5  \n",
              "83        9.4        5  \n",
              "86        9.9        6  \n",
              "88        9.5        5  \n",
              "91        9.9        6  \n",
              "92        9.8        5  \n",
              "106       9.3        5  \n",
              "151       9.4        4  \n",
              "161       9.2        4  \n",
              "169       9.5        5  \n",
              "181       9.3        5  \n",
              "201       9.3        5  \n",
              "226       9.5        6  \n",
              "240       9.3        5  \n",
              "258       9.4        5  \n",
              "281       9.9        7  \n",
              "338      11.5        6  \n",
              "339       9.8        7  \n",
              "340       9.2        6  \n",
              "369      12.0        7  \n",
              "372      10.6        6  \n",
              "376      11.0        6  \n",
              "377      12.0        7  \n",
              "415       9.3        5  \n",
              "451       9.1        6  \n",
              "477      11.0        6  \n",
              "482       9.4        5  \n",
              "483       9.4        5  \n",
              "503      10.9        7  \n",
              "504      10.8        7  \n",
              "506      10.8        7  \n",
              "515       9.3        5  \n",
              "586      10.6        7  \n",
              "614      10.2        6  \n",
              "639      10.3        6  \n",
              "689       9.4        5  \n",
              "692       9.0        5  \n",
              "723       9.5        5  \n",
              "754       9.1        6  \n",
              "795      10.2        5  \n",
              "852       9.7        5  \n",
              "1051      9.2        5  \n",
              "1158     10.6        6  \n",
              "1165      9.4        5  \n",
              "1260      9.3        5  \n",
              "1288     10.2        5  \n",
              "1289     10.2        5  \n",
              "1319      9.1        6  \n",
              "1367     10.5        6  \n",
              "1370      9.2        5  \n",
              "1371     11.6        6  \n",
              "1372      9.2        5  \n",
              "1403     10.0        8  \n",
              "1408     12.4        7  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bc2e284e-0ab7-4251-ba0b-1218de8beb41\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "        vertical-align: top;\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.610</td>\n",
              "      <td>0.29</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.114</td>\n",
              "      <td>9.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0.99740</td>\n",
              "      <td>3.26</td>\n",
              "      <td>1.56</td>\n",
              "      <td>9.1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>8.1</td>\n",
              "      <td>0.560</td>\n",
              "      <td>0.28</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.368</td>\n",
              "      <td>16.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>0.99680</td>\n",
              "      <td>3.11</td>\n",
              "      <td>1.28</td>\n",
              "      <td>9.3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>7.9</td>\n",
              "      <td>0.320</td>\n",
              "      <td>0.51</td>\n",
              "      <td>1.8</td>\n",
              "      <td>0.341</td>\n",
              "      <td>17.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>0.99690</td>\n",
              "      <td>3.04</td>\n",
              "      <td>1.08</td>\n",
              "      <td>9.2</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>8.1</td>\n",
              "      <td>0.660</td>\n",
              "      <td>0.22</td>\n",
              "      <td>2.2</td>\n",
              "      <td>0.069</td>\n",
              "      <td>9.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0.99680</td>\n",
              "      <td>3.30</td>\n",
              "      <td>1.20</td>\n",
              "      <td>10.3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>8.3</td>\n",
              "      <td>0.625</td>\n",
              "      <td>0.20</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.080</td>\n",
              "      <td>27.0</td>\n",
              "      <td>119.0</td>\n",
              "      <td>0.99720</td>\n",
              "      <td>3.16</td>\n",
              "      <td>1.12</td>\n",
              "      <td>9.1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.430</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.464</td>\n",
              "      <td>22.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.99740</td>\n",
              "      <td>3.13</td>\n",
              "      <td>1.28</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>7.3</td>\n",
              "      <td>0.670</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.8</td>\n",
              "      <td>0.401</td>\n",
              "      <td>16.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.99690</td>\n",
              "      <td>3.16</td>\n",
              "      <td>1.14</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>8.6</td>\n",
              "      <td>0.490</td>\n",
              "      <td>0.28</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.110</td>\n",
              "      <td>20.0</td>\n",
              "      <td>136.0</td>\n",
              "      <td>0.99720</td>\n",
              "      <td>2.93</td>\n",
              "      <td>1.95</td>\n",
              "      <td>9.9</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>9.3</td>\n",
              "      <td>0.390</td>\n",
              "      <td>0.44</td>\n",
              "      <td>2.1</td>\n",
              "      <td>0.107</td>\n",
              "      <td>34.0</td>\n",
              "      <td>125.0</td>\n",
              "      <td>0.99780</td>\n",
              "      <td>3.14</td>\n",
              "      <td>1.22</td>\n",
              "      <td>9.5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>8.6</td>\n",
              "      <td>0.490</td>\n",
              "      <td>0.28</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.110</td>\n",
              "      <td>20.0</td>\n",
              "      <td>136.0</td>\n",
              "      <td>0.99720</td>\n",
              "      <td>2.93</td>\n",
              "      <td>1.95</td>\n",
              "      <td>9.9</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>8.6</td>\n",
              "      <td>0.490</td>\n",
              "      <td>0.29</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.110</td>\n",
              "      <td>19.0</td>\n",
              "      <td>133.0</td>\n",
              "      <td>0.99720</td>\n",
              "      <td>2.93</td>\n",
              "      <td>1.98</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.410</td>\n",
              "      <td>0.68</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.467</td>\n",
              "      <td>18.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>0.99730</td>\n",
              "      <td>3.08</td>\n",
              "      <td>1.31</td>\n",
              "      <td>9.3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>9.2</td>\n",
              "      <td>0.520</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3.4</td>\n",
              "      <td>0.610</td>\n",
              "      <td>32.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>0.99960</td>\n",
              "      <td>2.74</td>\n",
              "      <td>2.00</td>\n",
              "      <td>9.4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>7.6</td>\n",
              "      <td>0.680</td>\n",
              "      <td>0.02</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.072</td>\n",
              "      <td>9.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.99650</td>\n",
              "      <td>3.17</td>\n",
              "      <td>1.08</td>\n",
              "      <td>9.2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>7.5</td>\n",
              "      <td>0.705</td>\n",
              "      <td>0.24</td>\n",
              "      <td>1.8</td>\n",
              "      <td>0.360</td>\n",
              "      <td>15.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>0.99640</td>\n",
              "      <td>3.00</td>\n",
              "      <td>1.59</td>\n",
              "      <td>9.5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>8.9</td>\n",
              "      <td>0.610</td>\n",
              "      <td>0.49</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.270</td>\n",
              "      <td>23.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>0.99720</td>\n",
              "      <td>3.12</td>\n",
              "      <td>1.02</td>\n",
              "      <td>9.3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>8.8</td>\n",
              "      <td>0.370</td>\n",
              "      <td>0.48</td>\n",
              "      <td>2.1</td>\n",
              "      <td>0.097</td>\n",
              "      <td>39.0</td>\n",
              "      <td>145.0</td>\n",
              "      <td>0.99750</td>\n",
              "      <td>3.04</td>\n",
              "      <td>1.03</td>\n",
              "      <td>9.3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>226</th>\n",
              "      <td>8.9</td>\n",
              "      <td>0.590</td>\n",
              "      <td>0.50</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.337</td>\n",
              "      <td>27.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>0.99640</td>\n",
              "      <td>3.04</td>\n",
              "      <td>1.61</td>\n",
              "      <td>9.5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240</th>\n",
              "      <td>8.9</td>\n",
              "      <td>0.635</td>\n",
              "      <td>0.37</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.263</td>\n",
              "      <td>5.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0.99710</td>\n",
              "      <td>3.00</td>\n",
              "      <td>1.09</td>\n",
              "      <td>9.3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>258</th>\n",
              "      <td>7.7</td>\n",
              "      <td>0.410</td>\n",
              "      <td>0.76</td>\n",
              "      <td>1.8</td>\n",
              "      <td>0.611</td>\n",
              "      <td>8.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>0.99680</td>\n",
              "      <td>3.06</td>\n",
              "      <td>1.26</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281</th>\n",
              "      <td>7.7</td>\n",
              "      <td>0.270</td>\n",
              "      <td>0.68</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0.358</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.99720</td>\n",
              "      <td>3.25</td>\n",
              "      <td>1.08</td>\n",
              "      <td>9.9</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>338</th>\n",
              "      <td>12.4</td>\n",
              "      <td>0.490</td>\n",
              "      <td>0.58</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.103</td>\n",
              "      <td>28.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>1.00080</td>\n",
              "      <td>3.16</td>\n",
              "      <td>1.00</td>\n",
              "      <td>11.5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>339</th>\n",
              "      <td>12.5</td>\n",
              "      <td>0.280</td>\n",
              "      <td>0.54</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.082</td>\n",
              "      <td>12.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0.99970</td>\n",
              "      <td>3.11</td>\n",
              "      <td>1.36</td>\n",
              "      <td>9.8</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>340</th>\n",
              "      <td>12.2</td>\n",
              "      <td>0.340</td>\n",
              "      <td>0.50</td>\n",
              "      <td>2.4</td>\n",
              "      <td>0.066</td>\n",
              "      <td>10.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>3.12</td>\n",
              "      <td>1.18</td>\n",
              "      <td>9.2</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>369</th>\n",
              "      <td>9.4</td>\n",
              "      <td>0.270</td>\n",
              "      <td>0.53</td>\n",
              "      <td>2.4</td>\n",
              "      <td>0.074</td>\n",
              "      <td>6.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.99620</td>\n",
              "      <td>3.20</td>\n",
              "      <td>1.13</td>\n",
              "      <td>12.0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>372</th>\n",
              "      <td>9.1</td>\n",
              "      <td>0.280</td>\n",
              "      <td>0.48</td>\n",
              "      <td>1.8</td>\n",
              "      <td>0.067</td>\n",
              "      <td>26.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>0.99670</td>\n",
              "      <td>3.32</td>\n",
              "      <td>1.04</td>\n",
              "      <td>10.6</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>376</th>\n",
              "      <td>11.5</td>\n",
              "      <td>0.450</td>\n",
              "      <td>0.50</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.078</td>\n",
              "      <td>19.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1.00030</td>\n",
              "      <td>3.26</td>\n",
              "      <td>1.11</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>377</th>\n",
              "      <td>9.4</td>\n",
              "      <td>0.270</td>\n",
              "      <td>0.53</td>\n",
              "      <td>2.4</td>\n",
              "      <td>0.074</td>\n",
              "      <td>6.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.99620</td>\n",
              "      <td>3.20</td>\n",
              "      <td>1.13</td>\n",
              "      <td>12.0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>8.6</td>\n",
              "      <td>0.725</td>\n",
              "      <td>0.24</td>\n",
              "      <td>6.6</td>\n",
              "      <td>0.117</td>\n",
              "      <td>31.0</td>\n",
              "      <td>134.0</td>\n",
              "      <td>1.00140</td>\n",
              "      <td>3.32</td>\n",
              "      <td>1.07</td>\n",
              "      <td>9.3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>451</th>\n",
              "      <td>8.4</td>\n",
              "      <td>0.370</td>\n",
              "      <td>0.53</td>\n",
              "      <td>1.8</td>\n",
              "      <td>0.413</td>\n",
              "      <td>9.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0.99790</td>\n",
              "      <td>3.06</td>\n",
              "      <td>1.06</td>\n",
              "      <td>9.1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>477</th>\n",
              "      <td>10.4</td>\n",
              "      <td>0.240</td>\n",
              "      <td>0.49</td>\n",
              "      <td>1.8</td>\n",
              "      <td>0.075</td>\n",
              "      <td>6.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.99770</td>\n",
              "      <td>3.18</td>\n",
              "      <td>1.06</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>482</th>\n",
              "      <td>10.6</td>\n",
              "      <td>0.360</td>\n",
              "      <td>0.59</td>\n",
              "      <td>2.2</td>\n",
              "      <td>0.152</td>\n",
              "      <td>6.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.99860</td>\n",
              "      <td>3.04</td>\n",
              "      <td>1.05</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>483</th>\n",
              "      <td>10.6</td>\n",
              "      <td>0.360</td>\n",
              "      <td>0.60</td>\n",
              "      <td>2.2</td>\n",
              "      <td>0.152</td>\n",
              "      <td>7.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.99860</td>\n",
              "      <td>3.04</td>\n",
              "      <td>1.06</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>503</th>\n",
              "      <td>10.5</td>\n",
              "      <td>0.260</td>\n",
              "      <td>0.47</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.078</td>\n",
              "      <td>6.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0.99760</td>\n",
              "      <td>3.18</td>\n",
              "      <td>1.04</td>\n",
              "      <td>10.9</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>504</th>\n",
              "      <td>10.5</td>\n",
              "      <td>0.240</td>\n",
              "      <td>0.42</td>\n",
              "      <td>1.8</td>\n",
              "      <td>0.077</td>\n",
              "      <td>6.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.99760</td>\n",
              "      <td>3.21</td>\n",
              "      <td>1.05</td>\n",
              "      <td>10.8</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>506</th>\n",
              "      <td>10.4</td>\n",
              "      <td>0.240</td>\n",
              "      <td>0.46</td>\n",
              "      <td>1.8</td>\n",
              "      <td>0.075</td>\n",
              "      <td>6.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>0.99760</td>\n",
              "      <td>3.25</td>\n",
              "      <td>1.02</td>\n",
              "      <td>10.8</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>515</th>\n",
              "      <td>8.5</td>\n",
              "      <td>0.655</td>\n",
              "      <td>0.49</td>\n",
              "      <td>6.1</td>\n",
              "      <td>0.122</td>\n",
              "      <td>34.0</td>\n",
              "      <td>151.0</td>\n",
              "      <td>1.00100</td>\n",
              "      <td>3.31</td>\n",
              "      <td>1.14</td>\n",
              "      <td>9.3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>586</th>\n",
              "      <td>11.1</td>\n",
              "      <td>0.310</td>\n",
              "      <td>0.49</td>\n",
              "      <td>2.7</td>\n",
              "      <td>0.094</td>\n",
              "      <td>16.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>0.99860</td>\n",
              "      <td>3.12</td>\n",
              "      <td>1.02</td>\n",
              "      <td>10.6</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>614</th>\n",
              "      <td>9.2</td>\n",
              "      <td>0.755</td>\n",
              "      <td>0.18</td>\n",
              "      <td>2.2</td>\n",
              "      <td>0.148</td>\n",
              "      <td>10.0</td>\n",
              "      <td>103.0</td>\n",
              "      <td>0.99690</td>\n",
              "      <td>2.87</td>\n",
              "      <td>1.36</td>\n",
              "      <td>10.2</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>639</th>\n",
              "      <td>8.9</td>\n",
              "      <td>0.290</td>\n",
              "      <td>0.35</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.067</td>\n",
              "      <td>25.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>0.99700</td>\n",
              "      <td>3.18</td>\n",
              "      <td>1.36</td>\n",
              "      <td>10.3</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>689</th>\n",
              "      <td>8.1</td>\n",
              "      <td>0.380</td>\n",
              "      <td>0.48</td>\n",
              "      <td>1.8</td>\n",
              "      <td>0.157</td>\n",
              "      <td>5.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.99760</td>\n",
              "      <td>3.30</td>\n",
              "      <td>1.05</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>692</th>\n",
              "      <td>8.6</td>\n",
              "      <td>0.490</td>\n",
              "      <td>0.51</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.422</td>\n",
              "      <td>16.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0.99790</td>\n",
              "      <td>3.03</td>\n",
              "      <td>1.17</td>\n",
              "      <td>9.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>723</th>\n",
              "      <td>7.1</td>\n",
              "      <td>0.310</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.2</td>\n",
              "      <td>0.053</td>\n",
              "      <td>36.0</td>\n",
              "      <td>127.0</td>\n",
              "      <td>0.99650</td>\n",
              "      <td>2.94</td>\n",
              "      <td>1.62</td>\n",
              "      <td>9.5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>754</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.480</td>\n",
              "      <td>0.68</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.415</td>\n",
              "      <td>14.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0.99656</td>\n",
              "      <td>3.09</td>\n",
              "      <td>1.06</td>\n",
              "      <td>9.1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>795</th>\n",
              "      <td>10.8</td>\n",
              "      <td>0.890</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.132</td>\n",
              "      <td>7.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.99786</td>\n",
              "      <td>2.99</td>\n",
              "      <td>1.18</td>\n",
              "      <td>10.2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>852</th>\n",
              "      <td>8.0</td>\n",
              "      <td>0.420</td>\n",
              "      <td>0.32</td>\n",
              "      <td>2.5</td>\n",
              "      <td>0.080</td>\n",
              "      <td>26.0</td>\n",
              "      <td>122.0</td>\n",
              "      <td>0.99801</td>\n",
              "      <td>3.22</td>\n",
              "      <td>1.07</td>\n",
              "      <td>9.7</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1051</th>\n",
              "      <td>8.5</td>\n",
              "      <td>0.460</td>\n",
              "      <td>0.59</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.414</td>\n",
              "      <td>16.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>0.99702</td>\n",
              "      <td>3.03</td>\n",
              "      <td>1.34</td>\n",
              "      <td>9.2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1158</th>\n",
              "      <td>6.7</td>\n",
              "      <td>0.410</td>\n",
              "      <td>0.43</td>\n",
              "      <td>2.8</td>\n",
              "      <td>0.076</td>\n",
              "      <td>22.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.99572</td>\n",
              "      <td>3.42</td>\n",
              "      <td>1.16</td>\n",
              "      <td>10.6</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1165</th>\n",
              "      <td>8.5</td>\n",
              "      <td>0.440</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.369</td>\n",
              "      <td>15.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0.99634</td>\n",
              "      <td>3.01</td>\n",
              "      <td>1.10</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1260</th>\n",
              "      <td>8.6</td>\n",
              "      <td>0.635</td>\n",
              "      <td>0.68</td>\n",
              "      <td>1.8</td>\n",
              "      <td>0.403</td>\n",
              "      <td>19.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>0.99632</td>\n",
              "      <td>3.02</td>\n",
              "      <td>1.15</td>\n",
              "      <td>9.3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1288</th>\n",
              "      <td>7.0</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.30</td>\n",
              "      <td>4.5</td>\n",
              "      <td>0.068</td>\n",
              "      <td>20.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>0.99914</td>\n",
              "      <td>3.30</td>\n",
              "      <td>1.17</td>\n",
              "      <td>10.2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1289</th>\n",
              "      <td>7.0</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.30</td>\n",
              "      <td>4.5</td>\n",
              "      <td>0.068</td>\n",
              "      <td>20.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>0.99914</td>\n",
              "      <td>3.30</td>\n",
              "      <td>1.17</td>\n",
              "      <td>10.2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1319</th>\n",
              "      <td>9.1</td>\n",
              "      <td>0.760</td>\n",
              "      <td>0.68</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.414</td>\n",
              "      <td>18.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>0.99652</td>\n",
              "      <td>2.90</td>\n",
              "      <td>1.33</td>\n",
              "      <td>9.1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1367</th>\n",
              "      <td>6.9</td>\n",
              "      <td>0.540</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.2</td>\n",
              "      <td>0.088</td>\n",
              "      <td>9.0</td>\n",
              "      <td>105.0</td>\n",
              "      <td>0.99725</td>\n",
              "      <td>3.25</td>\n",
              "      <td>1.18</td>\n",
              "      <td>10.5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1370</th>\n",
              "      <td>8.7</td>\n",
              "      <td>0.780</td>\n",
              "      <td>0.51</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.415</td>\n",
              "      <td>12.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>0.99623</td>\n",
              "      <td>3.00</td>\n",
              "      <td>1.17</td>\n",
              "      <td>9.2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1371</th>\n",
              "      <td>7.5</td>\n",
              "      <td>0.580</td>\n",
              "      <td>0.56</td>\n",
              "      <td>3.1</td>\n",
              "      <td>0.153</td>\n",
              "      <td>5.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.99476</td>\n",
              "      <td>3.21</td>\n",
              "      <td>1.03</td>\n",
              "      <td>11.6</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1372</th>\n",
              "      <td>8.7</td>\n",
              "      <td>0.780</td>\n",
              "      <td>0.51</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.415</td>\n",
              "      <td>12.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>0.99623</td>\n",
              "      <td>3.00</td>\n",
              "      <td>1.17</td>\n",
              "      <td>9.2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1403</th>\n",
              "      <td>7.2</td>\n",
              "      <td>0.330</td>\n",
              "      <td>0.33</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.061</td>\n",
              "      <td>3.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.99600</td>\n",
              "      <td>3.23</td>\n",
              "      <td>1.10</td>\n",
              "      <td>10.0</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1408</th>\n",
              "      <td>8.1</td>\n",
              "      <td>0.290</td>\n",
              "      <td>0.36</td>\n",
              "      <td>2.2</td>\n",
              "      <td>0.048</td>\n",
              "      <td>35.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>0.99500</td>\n",
              "      <td>3.27</td>\n",
              "      <td>1.01</td>\n",
              "      <td>12.4</td>\n",
              "      <td>7</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-bc2e284e-0ab7-4251-ba0b-1218de8beb41')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\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",
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              "    .colab-df-buttons div {\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",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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            }
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 14\n",
        "ds = pd.read_csv('/content/drive/MyDrive/Datasets/titanic.csv')\n",
        "ds['Age'] = ds.groupby(['Sex', 'Pclass'])['Age'].transform(lamda x: x.fillna(x.median()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "3T_YMUPM3Rnz",
        "outputId": "1d8ece7b-5d70-4481-ebca-cf337d8a7a23"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Survived  Pclass                                               Name  \\\n",
              "0           0       3                             Mr. Owen Harris Braund   \n",
              "1           1       1  Mrs. John Bradley (Florence Briggs Thayer) Cum...   \n",
              "2           1       3                              Miss. Laina Heikkinen   \n",
              "3           1       1        Mrs. Jacques Heath (Lily May Peel) Futrelle   \n",
              "4           0       3                            Mr. William Henry Allen   \n",
              "..        ...     ...                                                ...   \n",
              "882         0       2                               Rev. Juozas Montvila   \n",
              "883         1       1                        Miss. Margaret Edith Graham   \n",
              "884         0       3                     Miss. Catherine Helen Johnston   \n",
              "885         1       1                               Mr. Karl Howell Behr   \n",
              "886         0       3                                 Mr. Patrick Dooley   \n",
              "\n",
              "        Sex   Age  Siblings/Spouses Aboard  Parents/Children Aboard     Fare  \n",
              "0      male  22.0                        1                        0   7.2500  \n",
              "1    female  38.0                        1                        0  71.2833  \n",
              "2    female  26.0                        0                        0   7.9250  \n",
              "3    female  35.0                        1                        0  53.1000  \n",
              "4      male  35.0                        0                        0   8.0500  \n",
              "..      ...   ...                      ...                      ...      ...  \n",
              "882    male  27.0                        0                        0  13.0000  \n",
              "883  female  19.0                        0                        0  30.0000  \n",
              "884  female   7.0                        1                        2  23.4500  \n",
              "885    male  26.0                        0                        0  30.0000  \n",
              "886    male  32.0                        0                        0   7.7500  \n",
              "\n",
              "[887 rows x 8 columns]"
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              "      <td>Mrs. John Bradley (Florence Briggs Thayer) Cum...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
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              "      <td>71.2833</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
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              "      <td>female</td>\n",
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              "      <td>Mr. William Henry Allen</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
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              "    <tr>\n",
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              "      <td>Mr. Karl Howell Behr</td>\n",
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              "<p>887 rows × 8 columns</p>\n",
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              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-37bd35e1-da3a-4101-91de-71c2b4a61497 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-37bd35e1-da3a-4101-91de-71c2b4a61497');\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-63c54d61-7f9d-46fc-82af-3563c0e82b63\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-63c54d61-7f9d-46fc-82af-3563c0e82b63')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-63c54d61-7f9d-46fc-82af-3563c0e82b63 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ds",
              "summary": "{\n  \"name\": \"ds\",\n  \"rows\": 887,\n  \"fields\": [\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 887,\n        \"samples\": [\n          \"Mr. Adolphe Saalfeld\",\n          \"Mr. Joseph Philippe Lemercier Laroche\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.12190840546256,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          33.0,\n          9.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Siblings/Spouses Aboard\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parents/Children Aboard\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.78204040017391,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 15\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "ds = pd.read_csv('/content/drive/MyDrive/Datasets/WHO-COVID-19-global-data.csv')\n",
        "sum_cases = ds.groupby('Country_code').sum()['Cumulative_cases'].sort_values(ascending=False)\n",
        "sns.barplot(sum_cases[0:6], color='yellow')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "YaxE9VAnF7uc",
        "outputId": "337edd68-0929-4d65-f2ed-b86d26336ffa"
      },
      "execution_count": 122,
      "outputs": [
        {
          "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": [
        "# 16\n",
        "ds = pd.read_csv('/content/drive/MyDrive/Datasets/titanic.csv')\n",
        "print(ds['Fare'].skew())\n",
        "print(ds['Fare'].kurtosis())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yl3Wxd8QCmjI",
        "outputId": "dad487c0-c082-410c-9711-12d2c4105348"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4.777671399019824\n",
            "33.26460490128922\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 17\n",
        "from scipy.stats import mannwhitneyu\n",
        "\n",
        "group1 = ds[ds['Survived'] == 1]['Age'].dropna()\n",
        "group2 = ds[ds['Survived'] == 0]['Age'].dropna()\n",
        "\n",
        "stat, p = mannwhitneyu(group1, group2)\n",
        "\n",
        "print(f'Statistics: {stat}, p-value: {p}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IlGusLKtELQY",
        "outputId": "4aacbdd1-7181-4902-bc08-61acd6d457b2"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Statistics: 89850.5, p-value: 0.36773665395476707\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 18\n",
        "import numpy as np\n",
        "\n",
        "replace_dict = {'k': 1000, 'M': 1000000}\n",
        "\n",
        "def replace_and_multiply(x):\n",
        "  if pd.isnull(x):\n",
        "    return np.nan\n",
        "  else:\n",
        "    unit = x[-1]\n",
        "    number = float(x[:-1])\n",
        "    return number * replace_dict.get(unit, 1)\n",
        "\n",
        "gdp = pd.read_csv('/content/drive/MyDrive/Datasets/gdp_pcap.csv')\n",
        "pop = pd.read_csv('/content/drive/MyDrive/Datasets/pop.csv')\n",
        "lex = pd.read_csv('/content/drive/MyDrive/Datasets/lex.csv')\n",
        "\n",
        "ds = pd.DataFrame([gdp.iloc[:, -1], pop.iloc[:, -1], lex.iloc[:, -1]]).transpose()\n",
        "ds.columns = ['gdp', 'pop', 'lex']\n",
        "\n",
        "for serie in ['gdp', 'pop']:\n",
        "  ds[serie] = ds[serie].apply(replace_and_multiply)\n",
        "\n",
        "ds.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "67La9udQHbo6",
        "outputId": "dfe7bb01-9cdc-4e55-ea62-fca25ef578b4"
      },
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          gdp       pop       lex\n",
              "gdp  1.000000 -0.031582  0.163765\n",
              "pop -0.031582  1.000000  0.027512\n",
              "lex  0.163765  0.027512  1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-789ebf6c-4f2f-4fff-8066-d41705c05545\" 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>gdp</th>\n",
              "      <th>pop</th>\n",
              "      <th>lex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>gdp</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.031582</td>\n",
              "      <td>0.163765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pop</th>\n",
              "      <td>-0.031582</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.027512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lex</th>\n",
              "      <td>0.163765</td>\n",
              "      <td>0.027512</td>\n",
              "      <td>1.000000</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"ds\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.547967671128216,\n        \"min\": -0.031582060462233004,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.0,\n          -0.031582060462233004,\n          0.163764676319285\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5792792571644428,\n        \"min\": -0.031582060462233004,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -0.031582060462233004,\n          1.0,\n          0.027511900868352614\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5265591828626923,\n        \"min\": 0.027511900868352614,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.163764676319285,\n          0.027511900868352614,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 19\n",
        "ds = pd.read_csv('/content/drive/MyDrive/Datasets/winequality-red.csv', sep=';')\n",
        "percentile = ds['total sulfur dioxide'].quantile(0.95)\n",
        "ds[ds['total sulfur dioxide'] > percentile]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "id": "1VRe7EFgQlJ5",
        "outputId": "1794897a-6f90-453a-95cc-d0a4d987ba8b"
      },
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "14              8.9             0.620         0.18             3.8      0.176   \n",
              "15              8.9             0.620         0.19             3.9      0.170   \n",
              "32              8.3             0.655         0.12             2.3      0.083   \n",
              "46              7.7             0.935         0.43             2.2      0.114   \n",
              "53              8.6             0.380         0.36             3.0      0.081   \n",
              "...             ...               ...          ...             ...        ...   \n",
              "1496            7.7             0.540         0.26             1.9      0.089   \n",
              "1558            6.9             0.630         0.33             6.7      0.235   \n",
              "1559            7.8             0.600         0.26             2.0      0.080   \n",
              "1560            7.8             0.600         0.26             2.0      0.080   \n",
              "1561            7.8             0.600         0.26             2.0      0.080   \n",
              "\n",
              "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "14                   52.0                 145.0  0.99860  3.16       0.88   \n",
              "15                   51.0                 148.0  0.99860  3.17       0.93   \n",
              "32                   15.0                 113.0  0.99660  3.17       0.66   \n",
              "46                   22.0                 114.0  0.99700  3.25       0.73   \n",
              "53                   30.0                 119.0  0.99700  3.20       0.56   \n",
              "...                   ...                   ...      ...   ...        ...   \n",
              "1496                 23.0                 147.0  0.99636  3.26       0.59   \n",
              "1558                 66.0                 115.0  0.99787  3.22       0.56   \n",
              "1559                 31.0                 131.0  0.99622  3.21       0.52   \n",
              "1560                 31.0                 131.0  0.99622  3.21       0.52   \n",
              "1561                 31.0                 131.0  0.99622  3.21       0.52   \n",
              "\n",
              "      alcohol  quality  \n",
              "14        9.2        5  \n",
              "15        9.2        5  \n",
              "32        9.8        5  \n",
              "46        9.2        5  \n",
              "53        9.4        5  \n",
              "...       ...      ...  \n",
              "1496      9.7        5  \n",
              "1558      9.5        5  \n",
              "1559      9.9        5  \n",
              "1560      9.9        5  \n",
              "1561      9.9        5  \n",
              "\n",
              "[80 rows x 12 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>8.9</td>\n",
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              "      <th>53</th>\n",
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              "      <td>3.21</td>\n",
              "      <td>0.52</td>\n",
              "      <td>9.9</td>\n",
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            }
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 20\n",
        "\n",
        "# No 'New_tests' column\n",
        "\n",
        "# from sklearn.linear_model import LinearRegression\n",
        "\n",
        "# covid = pd.read_csv('/content/drive/MyDrive/Datasets/WHO-COVID-19-global-data.csv')\n",
        "# ds = covid[['New_cases']]\n",
        "\n",
        "# model = LinearRegression()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "WrFXIAeYRP2W",
        "outputId": "94ba7264-bb21-4e62-e5cd-5f2eec1cb3f6"
      },
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Date_reported Country_code      Country WHO_region  New_cases  \\\n",
              "0        2020-01-05           AF  Afghanistan       EMRO        NaN   \n",
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              "4        2020-02-02           AF  Afghanistan       EMRO        NaN   \n",
              "...             ...          ...          ...        ...        ...   \n",
              "53515    2024-03-10           ZW     Zimbabwe       AFRO        1.0   \n",
              "53516    2024-03-17           ZW     Zimbabwe       AFRO        NaN   \n",
              "53517    2024-03-24           ZW     Zimbabwe       AFRO        NaN   \n",
              "53518    2024-03-31           ZW     Zimbabwe       AFRO        NaN   \n",
              "53519    2024-04-07           ZW     Zimbabwe       AFRO        NaN   \n",
              "\n",
              "       Cumulative_cases  New_deaths  Cumulative_deaths  \n",
              "0                     0         NaN                  0  \n",
              "1                     0         NaN                  0  \n",
              "2                     0         NaN                  0  \n",
              "3                     0         NaN                  0  \n",
              "4                     0         NaN                  0  \n",
              "...                 ...         ...                ...  \n",
              "53515            266359         NaN               5740  \n",
              "53516            266359         NaN               5740  \n",
              "53517            266359         NaN               5740  \n",
              "53518            266359         NaN               5740  \n",
              "53519            266359         NaN               5740  \n",
              "\n",
              "[53520 rows x 8 columns]"
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              "  </tbody>\n",
              "</table>\n",
              "<p>53520 rows × 8 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fad01e51-6cc3-4859-8398-a83ea01a3ab9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fad01e51-6cc3-4859-8398-a83ea01a3ab9 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-fad01e51-6cc3-4859-8398-a83ea01a3ab9');\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-bffe3814-d38b-4c03-882d-c0c03b278fa2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bffe3814-d38b-4c03-882d-c0c03b278fa2')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bffe3814-d38b-4c03-882d-c0c03b278fa2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "covid",
              "summary": "{\n  \"name\": \"covid\",\n  \"rows\": 53520,\n  \"fields\": [\n    {\n      \"column\": \"Date_reported\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 223,\n        \"samples\": [\n          \"2020-03-08\",\n          \"2021-08-15\",\n          \"2022-04-03\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country_code\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 239,\n        \"samples\": [\n          \"BO\",\n          \"AI\",\n          \"GW\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 240,\n        \"samples\": [\n          \"Bolivia (Plurinational State of)\",\n          \"Anguilla\",\n          \"Guinea-Bissau\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WHO_region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"EMRO\",\n          \"EURO\",\n          \"SEARO\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_cases\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 276273.46502608486,\n        \"min\": -65079.0,\n        \"max\": 40475477.0,\n        \"num_unique_values\": 11918,\n        \"samples\": [\n          21024.0,\n          61080.0,\n          4373.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cumulative_cases\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7435734,\n        \"min\": 0,\n        \"max\": 103436829,\n        \"num_unique_values\": 32549,\n        \"samples\": [\n          17083,\n          202163,\n          39319\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1224.958678976594,\n        \"min\": -3432.0,\n        \"max\": 47687.0,\n        \"num_unique_values\": 2137,\n        \"samples\": [\n          744.0,\n          129.0,\n          383.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cumulative_deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 79595,\n        \"min\": 0,\n        \"max\": 1184883,\n        \"num_unique_values\": 13922,\n        \"samples\": [\n          4257,\n          27096,\n          153627\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    }
  ]
}