Practice_Week2.ipynb

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        "Machine Learning For AI"
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        "import pandas as pd"
      ]
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        "group4 = pd.DataFrame(\n",
        "    {'Name': ['hyojin','kyunghoon', 'Fran', 'Stefan'],\n",
        "     'Hobby': ['listening music','going home', 'making music', 'sailing'],\n",
        "     'Nationality':['Korea','Korea', 'Spain', 'Norway'],\n",
        "     'Major':['Statistics','Math', 'Software', 'Computer Science'],\n",
        "     'Age': [24,25,20,23]})"
      ],
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              "        Name            Hobby Nationality             Major  Age\n",
              "0     hyojin  listening music       Korea        Statistics   24\n",
              "1  kyunghoon       going home       Korea              Math   25\n",
              "2       Fran     making music       Spain          Software   20\n",
              "3     Stefan          sailing      Norway  Computer Science   23"
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              "      <td>24</td>\n",
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              "      <td>kyunghoon</td>\n",
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              "                Name            Hobby Nationality             Major  Age\n",
              "Name                                                                    \n",
              "hyojin        hyojin  listening music       Korea        Statistics   24\n",
              "kyunghoon  kyunghoon       going home       Korea              Math   25\n",
              "Fran            Fran     making music       Spain          Software   20\n",
              "Stefan        Stefan          sailing      Norway  Computer Science   23"
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              "      <th>hyojin</th>\n",
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              "      <td>listening music</td>\n",
              "      <td>Korea</td>\n",
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              "      <td>24</td>\n",
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              "      <th>kyunghoon</th>\n",
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              "      <td>going home</td>\n",
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              "        Name            Hobby Nationality             Major  Age\n",
              "0     hyojin  listening music       Korea        Statistics   24\n",
              "1  kyunghoon       going home       Korea              Math   25\n",
              "2       Fran     making music       Spain          Software   20\n",
              "3     Stefan          sailing      Norway  Computer Science   23"
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      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",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5e0e3d4f-dd96-4b58-80b8-cb9392dd8d18 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5e0e3d4f-dd96-4b58-80b8-cb9392dd8d18');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "group4['Age'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mSYMW9kv4d13",
        "outputId": "660e7117-d521-4ba2-d43d-b610e89a73d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23.0"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "group4['Age'].max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U6pMfBOc4kfT",
        "outputId": "3b31f583-d0ec-4180-cdfa-3675b221323d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "group4['Age'].var()"
      ],
      "metadata": {
        "id": "RsPtH3704qsh",
        "outputId": "93af0160-7f1c-48b6-cb94-89710730019f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.666666666666667"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "group4['Age'].sum()"
      ],
      "metadata": {
        "id": "cqa2AEBe4sco",
        "outputId": "4b1f7cac-97f2-4edb-848c-2917dc12f372",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "92"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    }
  ]
}