ch02.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cu9renI2pjcS"
      },
      "source": [
        "# 2장 데이터 적재"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CFxBlEPypjcX"
      },
      "source": [
        "<table align=\"left\"><tr><td>\n",
        "<a href=\"https://colab.research.google.com/github/rickiepark/ml-with-python-cookbook-2nd/blob/main/ch02.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"코랩에서 실행하기\"/></a>\n",
        "</td></tr></table>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandavro pymysql"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nbcOuyk6Xacj",
        "outputId": "ac5f38d9-bfff-467f-82a0-e9e77290d5c0"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pandavro\n",
            "  Downloading pandavro-1.8.0-py3-none-any.whl.metadata (8.5 kB)\n",
            "Collecting pymysql\n",
            "  Downloading PyMySQL-1.1.1-py3-none-any.whl.metadata (4.4 kB)\n",
            "Collecting fastavro<2.0.0,>=1.5.1 (from pandavro)\n",
            "  Downloading fastavro-1.10.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.5 kB)\n",
            "Requirement already satisfied: pandas>=1.1 in /usr/local/lib/python3.10/dist-packages (from pandavro) (2.2.2)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.10/dist-packages (from pandavro) (1.26.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1->pandavro) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1->pandavro) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1->pandavro) (2024.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=1.1->pandavro) (1.17.0)\n",
            "Downloading pandavro-1.8.0-py3-none-any.whl (8.8 kB)\n",
            "Downloading PyMySQL-1.1.1-py3-none-any.whl (44 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.0/45.0 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading fastavro-1.10.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m33.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: pymysql, fastavro, pandavro\n",
            "Successfully installed fastavro-1.10.0 pandavro-1.8.0 pymysql-1.1.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dWedlSikXPmk",
        "outputId": "a1519d81-58ef-49ba-bf40-f29f2a102692"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sklearn 1.6.0\n",
            "numpy 1.26.4\n",
            "pandas 2.2.2\n",
            "matplotlib 3.8.0\n",
            "openpyxl 3.1.5\n",
            "pyarrow 17.0.0\n",
            "pandavro                           1.8.0\n",
            "sqlalchemy 2.0.36\n",
            "pymysql 1.4.6\n"
          ]
        }
      ],
      "source": [
        "import sklearn\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib\n",
        "import openpyxl\n",
        "import pyarrow\n",
        "import pandavro\n",
        "import sqlalchemy\n",
        "import pymysql\n",
        "\n",
        "print('sklearn', sklearn.__version__)\n",
        "print('numpy', np.__version__)\n",
        "print('pandas', pd.__version__)\n",
        "print('matplotlib', matplotlib.__version__)\n",
        "print('openpyxl', openpyxl.__version__)\n",
        "print('pyarrow', pyarrow.__version__)\n",
        "!pip list | grep pandavro\n",
        "print('sqlalchemy', sqlalchemy.__version__)\n",
        "print('pymysql', pymysql.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eLl2jIDnpjcY"
      },
      "source": [
        "## 2.1 샘플 데이터셋 적재"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s0w8gUB-pjcZ",
        "outputId": "61f7ce92-205b-4e98-833a-d6ec57f5281b",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.,  0.,  5., 13.,  9.,  1.,  0.,  0.,  0.,  0., 13., 15., 10.,\n",
              "       15.,  5.,  0.,  0.,  3., 15.,  2.,  0., 11.,  8.,  0.,  0.,  4.,\n",
              "       12.,  0.,  0.,  8.,  8.,  0.,  0.,  5.,  8.,  0.,  0.,  9.,  8.,\n",
              "        0.,  0.,  4., 11.,  0.,  1., 12.,  7.,  0.,  0.,  2., 14.,  5.,\n",
              "       10., 12.,  0.,  0.,  0.,  0.,  6., 13., 10.,  0.,  0.,  0.])"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# 사이킷런의 데이터셋을 적재합니다.\n",
        "from sklearn import datasets\n",
        "\n",
        "# 숫자 데이터셋을 적재합니다.\n",
        "digits = datasets.load_digits()\n",
        "\n",
        "# 특성 행렬을 만듭니다.\n",
        "features = digits.data\n",
        "\n",
        "# 타깃 벡터를 만듭니다.\n",
        "target = digits.target\n",
        "\n",
        "# 첫 번째 샘플을 확인합니다.\n",
        "features[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TU5nNT2_XPmq",
        "outputId": "cf5009d9-65af-4f42-e06d-ca6efc758354"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".. _digits_dataset:\n",
            "\n",
            "Optical recognition of handwritten digits dataset\n",
            "--------------------------------------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            ":Number of Instances: 1797\n",
            ":Number of Attributes: 64\n",
            ":Attribute Information: 8x8 image of integer pixels in the range 0..16.\n",
            ":Missing Attribute Values: None\n",
            ":Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\n",
            ":Date: July; 1998\n",
            "\n",
            "This is a copy of the test set of the UCI ML hand-written digits datasets\n",
            "https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\n",
            "\n",
            "The data set contains images of hand-written digits: 10 classes where\n",
            "each class refers to a digit.\n",
            "\n",
            "Preprocessing programs made available by NIST were used to extract\n",
            "normalized bitmaps of handwritten digits from a preprinted form. From a\n",
            "total of 43 people, 30 contributed to the training set and different 13\n",
            "to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\n",
            "4x4 and the number of on pixels are counted in each block. This generates\n",
            "an input matrix of 8x8 where each element is an integer in the range\n",
            "0..16. This reduces dimensionality and gives invariance to small\n",
            "distortions.\n",
            "\n",
            "For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\n",
            "T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\n",
            "L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\n",
            "1994.\n",
            "\n",
            ".. dropdown:: References\n",
            "\n",
            "  - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\n",
            "    Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\n",
            "    Graduate Studies in Science and Engineering, Bogazici University.\n",
            "  - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\n",
            "  - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\n",
            "    Linear dimensionalityreduction using relevance weighted LDA. School of\n",
            "    Electrical and Electronic Engineering Nanyang Technological University.\n",
            "    2005.\n",
            "  - Claudio Gentile. A New Approximate Maximal Margin Classification\n",
            "    Algorithm. NIPS. 2000.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# 사이킷런 데이터셋을 임포트합니다.\n",
        "from sklearn import datasets\n",
        "\n",
        "# digits 데이터셋을 로드합니다.\n",
        "digits = datasets.load_digits()\n",
        "\n",
        "# DESCR 속성을 출력합니다.\n",
        "print(digits.DESCR)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iGt8O02spjcb"
      },
      "source": [
        "### 붙임"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uBrfdIr7pjcc",
        "outputId": "263cdebc-e9db-4729-d4f7-7d543c0922da"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'frame', 'feature_names', 'target_names', 'images', 'DESCR'])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "digits.keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "S1qkesBEpjce",
        "outputId": "645dad55-b381-4445-8c88-bf60dbcb6ada"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'.. _digits_dataset:\\n\\nOptical recognition of handwritten digits dataset'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# DESCR 키는 데이터셋에 대한 설명을 담고 있습니다.\n",
        "digits['DESCR'][:70]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dhnuWhBFpjcf",
        "outputId": "61576081-f5f8-4acb-d01d-9f8e26dbc103"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "# 0에서부터 4까지 다섯 개의 숫자만 적재합니다.\n",
        "X, y = datasets.load_digits(n_class=5, return_X_y=True)\n",
        "# 배열에 있는 고유한 값을 반환합니다.\n",
        "np.unique(y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w4CKzFqHpjcg"
      },
      "source": [
        "## 2.2 모의 데이터셋 만들기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sl0vjTnfpjch",
        "outputId": "0c454dc3-3ab9-419c-d2b7-6c24c890cd83"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "특성 행렬\n",
            " [[ 1.29322588 -0.61736206 -0.11044703]\n",
            " [-2.793085    0.36633201  1.93752881]\n",
            " [ 0.80186103 -0.18656977  0.0465673 ]]\n",
            "타깃 벡터\n",
            " [-10.37865986  25.5124503   19.67705609]\n"
          ]
        }
      ],
      "source": [
        "# 라이브러리를 임포트합니다.\n",
        "from sklearn.datasets import make_regression\n",
        "\n",
        "# 특성 행렬, 타깃 벡터, 정답 계수를 생성합니다.\n",
        "features, target, coefficients = make_regression(n_samples = 100,\n",
        "                                                 n_features = 3,\n",
        "                                                 n_informative = 3,\n",
        "                                                 n_targets = 1,\n",
        "                                                 noise = 0.0,\n",
        "                                                 coef = True,\n",
        "                                                 random_state = 1)\n",
        "\n",
        "# 특성 행렬과 타깃 벡터를 확인합니다.\n",
        "print('특성 행렬\\n', features[:3])\n",
        "print('타깃 벡터\\n', target[:3])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S6ogH04-pjci",
        "outputId": "3e0ff410-831f-4ca4-d70e-6c327777e568"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "특성 행렬\n",
            " [[ 1.06354768 -1.42632219  1.02163151]\n",
            " [ 0.23156977  1.49535261  0.33251578]\n",
            " [ 0.15972951  0.83533515 -0.40869554]]\n",
            "타깃 벡터\n",
            " [1 0 0]\n"
          ]
        }
      ],
      "source": [
        "# 라이브러리를 임포트합니다.\n",
        "from sklearn.datasets import make_classification\n",
        "\n",
        "# 특성 행렬과 타깃 벡터를 생성합니다.\n",
        "features, target = make_classification(n_samples = 100,\n",
        "                                       n_features = 3,\n",
        "                                       n_informative = 3,\n",
        "                                       n_redundant = 0,\n",
        "                                       n_classes = 2,\n",
        "                                       weights = [.25, .75],\n",
        "                                       random_state = 1)\n",
        "\n",
        "# 특성 행렬과 타깃 벡터를 확인합니다.\n",
        "print('특성 행렬\\n', features[:3])\n",
        "print('타깃 벡터\\n', target[:3])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h-Ig5Pmvpjci",
        "outputId": "2e7da456-283e-43b5-9b18-ce1e27541ec1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "특성 행렬\n",
            " [[ -1.22685609   3.25572052]\n",
            " [ -9.57463218  -4.38310652]\n",
            " [-10.71976941  -4.20558148]]\n",
            "타깃 벡터\n",
            " [0 1 1]\n"
          ]
        }
      ],
      "source": [
        "# 라이브러리를 임포트합니다.\n",
        "from sklearn.datasets import make_blobs\n",
        "\n",
        "# 특성 행렬과 타깃 벡터를 생성합니다.\n",
        "features, target = make_blobs(n_samples = 100,\n",
        "                              n_features = 2,\n",
        "                              centers = 3,\n",
        "                              cluster_std = 0.5,\n",
        "                              shuffle = True,\n",
        "                              random_state = 1)\n",
        "\n",
        "# 특성 행렬과 타깃 벡터를 확인합니다.\n",
        "print('특성 행렬\\n', features[:3])\n",
        "print('타깃 벡터\\n', target[:3])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "u8m9QdS9pjcj"
      },
      "outputs": [],
      "source": [
        "# 라이브러리를 임포트합니다.\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "ETOZLyZPpjcj",
        "outputId": "081bd4ca-7860-4f99-c361-0ef75a42a69f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 산점도를 출력합니다.\n",
        "plt.scatter(features[:,0], features[:,1], c=target)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oL097qjWpjcj"
      },
      "source": [
        "## 2.3 CSV 파일 적재"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "Iwm-_A_6pjcj",
        "outputId": "5e40cbfc-a5d2-4d1b-baca-e14effe45e87"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   integer             datetime  category\n",
              "0        5  2015-01-01 00:00:00         0\n",
              "1        5  2015-01-01 00:00:01         0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7684d156-bdeb-425c-b730-0b3021b15b80\" 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>integer</th>\n",
              "      <th>datetime</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5</td>\n",
              "      <td>2015-01-01 00:00:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>2015-01-01 00:00:01</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7684d156-bdeb-425c-b730-0b3021b15b80')\"\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-7684d156-bdeb-425c-b730-0b3021b15b80 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-7684d156-bdeb-425c-b730-0b3021b15b80');\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-83832e9f-7260-4656-99a2-9a39cf4fb178\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-83832e9f-7260-4656-99a2-9a39cf4fb178')\"\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-83832e9f-7260-4656-99a2-9a39cf4fb178 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataframe",
              "summary": "{\n  \"name\": \"dataframe\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"integer\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          4,\n          9,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"datetime\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 100,\n        \"samples\": [\n          \"2015-01-01 00:01:23\",\n          \"2015-01-01 00:00:53\",\n          \"2015-01-01 00:01:10\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# 라이브러리를 임포트합니다.\n",
        "import pandas as pd\n",
        "\n",
        "# 데이터 URL\n",
        "url = 'https://bit.ly/47Hl2t5'\n",
        "\n",
        "# 데이터 적재\n",
        "dataframe = pd.read_csv(url)\n",
        "\n",
        "# 처음 두 행을 확인합니다.\n",
        "dataframe.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RLmmW32-pjck"
      },
      "source": [
        "### 붙임"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "1KLPG0h8pjck",
        "outputId": "fa054747-8ffe-4f5f-c79d-704412d2f8d1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   integer             datetime  category\n",
              "0        5  2015-01-01 00:00:10         0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-39eb98e7-6e56-4c01-b5e1-fbd3b381bfa7\" 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>integer</th>\n",
              "      <th>datetime</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5</td>\n",
              "      <td>2015-01-01 00:00:10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-39eb98e7-6e56-4c01-b5e1-fbd3b381bfa7')\"\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-39eb98e7-6e56-4c01-b5e1-fbd3b381bfa7 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-39eb98e7-6e56-4c01-b5e1-fbd3b381bfa7');\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=\"id_7a7709c5-e05f-4264-b685-598a80495d2f\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('dataframe')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_7a7709c5-e05f-4264-b685-598a80495d2f button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('dataframe');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataframe",
              "summary": "{\n  \"name\": \"dataframe\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"integer\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 5,\n        \"max\": 5,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"datetime\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2015-01-01 00:00:10\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# 1~10번째 행을 건너 뛰고 한 행을 읽습니다.\n",
        "dataframe = pd.read_csv(url, skiprows=range(1, 11), nrows=1)\n",
        "dataframe"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-EtFTPcRpjck"
      },
      "source": [
        "## 2.4 엑셀 파일 적재"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "FZwPR-Mbpjck",
        "outputId": "5f13a056-b902-4eeb-fe7d-ca0ecaa92139"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   5 2015-01-01 00:00:00  0\n",
              "0  5 2015-01-01 00:00:01  0\n",
              "1  9 2015-01-01 00:00:02  0"
            ],
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              "      <th>2015-01-01 00:00:00</th>\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataframe",
              "summary": "{\n  \"name\": \"dataframe\",\n  \"rows\": 99,\n  \"fields\": [\n    {\n      \"column\": 5,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          4,\n          9,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2015-01-01 00:00:00\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2015-01-01 00:00:01\",\n        \"max\": \"2015-01-01 00:01:39\",\n        \"num_unique_values\": 99,\n        \"samples\": [\n          \"2015-01-01 00:01:03\",\n          \"2015-01-01 00:00:41\",\n          \"2015-01-01 00:01:36\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "# 라이브러리를 임포트합니다.\n",
        "import pandas as pd\n",
        "\n",
        "# 데이터 URL\n",
        "url = 'https://bit.ly/3V07wOm'\n",
        "\n",
        "# 데이터 적재\n",
        "dataframe = pd.read_excel(url, sheet_name=0, header=1)\n",
        "\n",
        "# 처음 두 행을 확인합니다.\n",
        "dataframe.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yxps7dXNpjcl"
      },
      "source": [
        "## 2.5 JSON 파일 적재하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "xlS4fwPypjcl",
        "outputId": "9f0fd940-f274-421f-c011-02e98626b3b5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   integer            datetime  category\n",
              "0        5 2015-01-01 00:00:00         0\n",
              "1        5 2015-01-01 00:00:01         0"
            ],
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-38f75c8f-2e97-4d39-a03e-200c1da5b5ae');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "  }\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",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    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-38db069e-8e5c-4f95-a462-339915eae736 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataframe",
              "summary": "{\n  \"name\": \"dataframe\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"integer\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          4,\n          9,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"datetime\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2015-01-01 00:00:00\",\n        \"max\": \"2015-01-01 00:01:39\",\n        \"num_unique_values\": 100,\n        \"samples\": [\n          \"2015-01-01 00:01:23\",\n          \"2015-01-01 00:00:53\",\n          \"2015-01-01 00:01:10\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# 라이브러리를 임포트합니다.\n",
        "import pandas as pd\n",
        "\n",
        "# 데이터 URL\n",
        "url = 'https://bit.ly/3IaJXer'\n",
        "\n",
        "# 데이터 적재\n",
        "dataframe = pd.read_json(url, orient='columns')\n",
        "\n",
        "# 처음 두 행을 확인합니다.\n",
        "dataframe.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1zh0CMUDXPm-"
      },
      "source": [
        "## 2.6 파케이 파일 적재하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "0y8tN8xyXPm-",
        "outputId": "a3939602-8649-4477-c6d1-c5f9c1bf29c1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   integer             datetime  category\n",
              "0        5  2015-01-01 00:00:00         0\n",
              "1        5  2015-01-01 00:00:01         0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ae58b402-fc75-4421-b073-fb99d097c9a2\" 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>integer</th>\n",
              "      <th>datetime</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5</td>\n",
              "      <td>2015-01-01 00:00:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>2015-01-01 00:00:01</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae58b402-fc75-4421-b073-fb99d097c9a2')\"\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-ae58b402-fc75-4421-b073-fb99d097c9a2 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-ae58b402-fc75-4421-b073-fb99d097c9a2');\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-a2474da3-6ecd-459a-bf06-ea4dc1966f74\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a2474da3-6ecd-459a-bf06-ea4dc1966f74')\"\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-a2474da3-6ecd-459a-bf06-ea4dc1966f74 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataframe",
              "summary": "{\n  \"name\": \"dataframe\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"integer\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          4,\n          9,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"datetime\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 100,\n        \"samples\": [\n          \"2015-01-01 00:01:23\",\n          \"2015-01-01 00:00:53\",\n          \"2015-01-01 00:01:10\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# 라이브러리를 임포트합니다.\n",
        "import pandas as pd\n",
        "\n",
        "# 데이터 URL\n",
        "url = 'https://bit.ly/3SMBLFY'\n",
        "\n",
        "# 데이터 적재\n",
        "dataframe = pd.read_parquet(url)\n",
        "\n",
        "# 처음 두 행을 확인합니다.\n",
        "dataframe.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iUxu6-p_XPm-"
      },
      "source": [
        "## 2.7 아브로 파일 적재하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "7WJasTUVXPm-",
        "outputId": "7dec6613-5c84-4f26-e000-a451f1b26166"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   integer             datetime  category\n",
              "0        5  2015-01-01 00:00:00         0\n",
              "1        5  2015-01-01 00:00:01         0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8c158755-e3c5-4f74-8451-90ac24bdb78e\" 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>integer</th>\n",
              "      <th>datetime</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5</td>\n",
              "      <td>2015-01-01 00:00:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>2015-01-01 00:00:01</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c158755-e3c5-4f74-8451-90ac24bdb78e')\"\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-8c158755-e3c5-4f74-8451-90ac24bdb78e 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-8c158755-e3c5-4f74-8451-90ac24bdb78e');\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-b50b0f14-c441-4a82-923a-6c2749191d4e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b50b0f14-c441-4a82-923a-6c2749191d4e')\"\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-b50b0f14-c441-4a82-923a-6c2749191d4e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataframe",
              "summary": "{\n  \"name\": \"dataframe\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"integer\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          4,\n          9,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"datetime\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 100,\n        \"samples\": [\n          \"2015-01-01 00:01:23\",\n          \"2015-01-01 00:00:53\",\n          \"2015-01-01 00:01:10\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# 라이브러리를 임포트합니다.\n",
        "import requests\n",
        "import pandavro as pdx\n",
        "\n",
        "# 데이터 URL\n",
        "url = 'https://bit.ly/42PLdx1'\n",
        "\n",
        "# 파일을 다운로드합니다.\n",
        "r = requests.get(url)\n",
        "open('data.avro', 'wb').write(r.content)\n",
        "\n",
        "# 데이터 적재\n",
        "dataframe = pdx.read_avro('data.avro')\n",
        "\n",
        "# 처음 두 행을 확인합니다.\n",
        "dataframe.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ia-6fCyupjcl"
      },
      "source": [
        "## 2.8 SQLite 데이터베이스로부터 적재하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "5B6oFRgGpjcl"
      },
      "outputs": [],
      "source": [
        "# 코랩에서 실행하는 경우 다음 주석을 제거하고 실행하세요.\n",
        "!wget -q https://bit.ly/3UTVSoy -O sample.db"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "2bgLzgu7pjcm",
        "outputId": "46db28cd-7b64-484d-dd06-b1f2caf6c83e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  first_name last_name  age  preTestScore  postTestScore\n",
              "0      Jason    Miller   42             4             25\n",
              "1      Molly  Jacobson   52            24             94"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a198c24f-ca6a-4653-a525-f1ba2133ba5a\" 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>first_name</th>\n",
              "      <th>last_name</th>\n",
              "      <th>age</th>\n",
              "      <th>preTestScore</th>\n",
              "      <th>postTestScore</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Jason</td>\n",
              "      <td>Miller</td>\n",
              "      <td>42</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Molly</td>\n",
              "      <td>Jacobson</td>\n",
              "      <td>52</td>\n",
              "      <td>24</td>\n",
              "      <td>94</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-a198c24f-ca6a-4653-a525-f1ba2133ba5a')\"\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-a198c24f-ca6a-4653-a525-f1ba2133ba5a 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-a198c24f-ca6a-4653-a525-f1ba2133ba5a');\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-7bc12dd8-7549-4848-b926-b1bdba6e7026\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7bc12dd8-7549-4848-b926-b1bdba6e7026')\"\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-7bc12dd8-7549-4848-b926-b1bdba6e7026 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataframe",
              "summary": "{\n  \"name\": \"dataframe\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"first_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Molly\",\n          \"Amy\",\n          \"Tina\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"last_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Jacobson\",\n          \"Cooze\",\n          \"Ali\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 24,\n        \"max\": 73,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          52,\n          73,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"preTestScore\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 2,\n        \"max\": 31,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          24,\n          3,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"postTestScore\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24,\n        \"min\": 25,\n        \"max\": 94,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          94,\n          70,\n          57\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# 라이브러리를 임포트합니다.\n",
        "import pandas as pd\n",
        "from sqlalchemy import create_engine\n",
        "\n",
        "# 데이터베이스에 연결합니다.\n",
        "database_connection = create_engine('sqlite:///sample.db')\n",
        "\n",
        "# 데이터를 적재합니다.\n",
        "dataframe = pd.read_sql_query('SELECT * FROM data', database_connection)\n",
        "\n",
        "# 처음 두 개의 행을 확인합니다.\n",
        "dataframe.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gerME5wXpjcm"
      },
      "source": [
        "### 붙임"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "aK8AeoGRpjcm",
        "outputId": "3fc44a88-8b2d-43dc-9204-82cf2bd69b45"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  first_name last_name  age  preTestScore  postTestScore\n",
              "0      Jason    Miller   42             4             25\n",
              "1      Molly  Jacobson   52            24             94"
            ],
            "text/html": [
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              "      height: 32px;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5f70fcad-d151-433a-98ed-59cf97da3dcd 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-5f70fcad-d151-433a-98ed-59cf97da3dcd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
              "<div id=\"df-9446bcc6-9437-495f-98ab-173214313681\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9446bcc6-9437-495f-98ab-173214313681')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9446bcc6-9437-495f-98ab-173214313681 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataframe",
              "summary": "{\n  \"name\": \"dataframe\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"first_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Molly\",\n          \"Amy\",\n          \"Tina\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"last_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Jacobson\",\n          \"Cooze\",\n          \"Ali\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 24,\n        \"max\": 73,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          52,\n          73,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"preTestScore\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 2,\n        \"max\": 31,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          24,\n          3,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"postTestScore\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24,\n        \"min\": 25,\n        \"max\": 94,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          94,\n          70,\n          57\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "# 테이블의 전체 데이터를 가져 옵니다.\n",
        "dataframe = pd.read_sql_table('data', database_connection)\n",
        "dataframe.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6sqVwFjuXPnC"
      },
      "source": [
        "## 2.9 원격 SQL 데이터베이스에 쿼리하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C28ztOnIXPnC",
        "outputId": "7ab86280-fa76-475a-d622-fffbbe2723a1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Preparing to unpack .../23-mecab-ipadic-utf8_2.7.0-20070801+main-3_all.deb ...\n",
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            "Selecting previously unselected package mysql-server.\n",
            "Preparing to unpack .../24-mysql-server_8.0.40-0ubuntu0.22.04.1_all.deb ...\n",
            "Unpacking mysql-server (8.0.40-0ubuntu0.22.04.1) ...\n",
            "Setting up libhttp-date-perl (6.05-1) ...\n",
            "Setting up libmecab2:amd64 (0.996-14build9) ...\n",
            "Setting up mysql-client-core-8.0 (8.0.40-0ubuntu0.22.04.1) ...\n",
            "Setting up libfcgi0ldbl:amd64 (2.4.2-2build2) ...\n",
            "Setting up libclone-perl (0.45-1build3) ...\n",
            "Setting up libhtml-tagset-perl (3.20-4) ...\n",
            "Setting up liblwp-mediatypes-perl (6.04-1) ...\n",
            "Setting up libfcgi-bin (2.4.2-2build2) ...\n",
            "Setting up libencode-locale-perl (1.05-1.1) ...\n",
            "Setting up libprotobuf-lite23:amd64 (3.12.4-1ubuntu7.22.04.1) ...\n",
            "Setting up mecab-utils (0.996-14build9) ...\n",
            "Setting up libio-html-perl (1.004-2) ...\n",
            "Setting up mysql-server-core-8.0 (8.0.40-0ubuntu0.22.04.1) ...\n",
            "Setting up mysql-client-8.0 (8.0.40-0ubuntu0.22.04.1) ...\n",
            "Setting up libfcgi-perl:amd64 (0.82+ds-1build1) ...\n",
            "Setting up liburi-perl (5.10-1) ...\n",
            "Setting up libhttp-message-perl (6.36-1) ...\n",
            "Setting up mysql-server-8.0 (8.0.40-0ubuntu0.22.04.1) ...\n",
            "invoke-rc.d: could not determine current runlevel\n",
            "invoke-rc.d: policy-rc.d denied execution of stop.\n",
            "update-alternatives: using /etc/mysql/mysql.cnf to provide /etc/mysql/my.cnf (my.cnf) in auto mode\n",
            "Renaming removed key_buffer and myisam-recover options (if present)\n",
            "mysqld will log errors to /var/log/mysql/error.log\n",
            "mysqld is running as pid 1104\n",
            "Created symlink /etc/systemd/system/multi-user.target.wants/mysql.service → /lib/systemd/system/mysql.service.\n",
            "invoke-rc.d: could not determine current runlevel\n",
            "invoke-rc.d: policy-rc.d denied execution of start.\n",
            "Setting up mecab-ipadic (2.7.0-20070801+main-3) ...\n",
            "Compiling IPA dictionary for Mecab.  This takes long time...\n",
            "reading /usr/share/mecab/dic/ipadic/unk.def ... 40\n",
            "emitting double-array: 100% |###########################################| \n",
            "/usr/share/mecab/dic/ipadic/model.def is not found. skipped.\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.adverbal.csv ... 795\n",
            "reading /usr/share/mecab/dic/ipadic/Postp.csv ... 146\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.demonst.csv ... 120\n",
            "reading /usr/share/mecab/dic/ipadic/Conjunction.csv ... 171\n",
            "reading /usr/share/mecab/dic/ipadic/Auxil.csv ... 199\n",
            "reading /usr/share/mecab/dic/ipadic/Adj.csv ... 27210\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.adjv.csv ... 3328\n",
            "reading /usr/share/mecab/dic/ipadic/Others.csv ... 2\n",
            "reading /usr/share/mecab/dic/ipadic/Interjection.csv ... 252\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.others.csv ... 151\n",
            "reading /usr/share/mecab/dic/ipadic/Symbol.csv ... 208\n",
            "reading /usr/share/mecab/dic/ipadic/Verb.csv ... 130750\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.verbal.csv ... 12146\n",
            "reading /usr/share/mecab/dic/ipadic/Postp-col.csv ... 91\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.nai.csv ... 42\n",
            "reading /usr/share/mecab/dic/ipadic/Adverb.csv ... 3032\n",
            "reading /usr/share/mecab/dic/ipadic/Suffix.csv ... 1393\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.proper.csv ... 27328\n",
            "reading /usr/share/mecab/dic/ipadic/Prefix.csv ... 221\n",
            "reading /usr/share/mecab/dic/ipadic/Filler.csv ... 19\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.org.csv ... 16668\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.number.csv ... 42\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.csv ... 60477\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.name.csv ... 34202\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.place.csv ... 72999\n",
            "reading /usr/share/mecab/dic/ipadic/Adnominal.csv ... 135\n",
            "emitting double-array: 100% |###########################################| \n",
            "reading /usr/share/mecab/dic/ipadic/matrix.def ... 1316x1316\n",
            "emitting matrix      : 100% |###########################################| \n",
            "\n",
            "done!\n",
            "update-alternatives: using /var/lib/mecab/dic/ipadic to provide /var/lib/mecab/dic/debian (mecab-dictionary) in auto mode\n",
            "Setting up mecab-ipadic-utf8 (2.7.0-20070801+main-3) ...\n",
            "Compiling IPA dictionary for Mecab.  This takes long time...\n",
            "reading /usr/share/mecab/dic/ipadic/unk.def ... 40\n",
            "emitting double-array: 100% |###########################################| \n",
            "/usr/share/mecab/dic/ipadic/model.def is not found. skipped.\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.adverbal.csv ... 795\n",
            "reading /usr/share/mecab/dic/ipadic/Postp.csv ... 146\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.demonst.csv ... 120\n",
            "reading /usr/share/mecab/dic/ipadic/Conjunction.csv ... 171\n",
            "reading /usr/share/mecab/dic/ipadic/Auxil.csv ... 199\n",
            "reading /usr/share/mecab/dic/ipadic/Adj.csv ... 27210\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.adjv.csv ... 3328\n",
            "reading /usr/share/mecab/dic/ipadic/Others.csv ... 2\n",
            "reading /usr/share/mecab/dic/ipadic/Interjection.csv ... 252\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.others.csv ... 151\n",
            "reading /usr/share/mecab/dic/ipadic/Symbol.csv ... 208\n",
            "reading /usr/share/mecab/dic/ipadic/Verb.csv ... 130750\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.verbal.csv ... 12146\n",
            "reading /usr/share/mecab/dic/ipadic/Postp-col.csv ... 91\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.nai.csv ... 42\n",
            "reading /usr/share/mecab/dic/ipadic/Adverb.csv ... 3032\n",
            "reading /usr/share/mecab/dic/ipadic/Suffix.csv ... 1393\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.proper.csv ... 27328\n",
            "reading /usr/share/mecab/dic/ipadic/Prefix.csv ... 221\n",
            "reading /usr/share/mecab/dic/ipadic/Filler.csv ... 19\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.org.csv ... 16668\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.number.csv ... 42\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.csv ... 60477\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.name.csv ... 34202\n",
            "reading /usr/share/mecab/dic/ipadic/Noun.place.csv ... 72999\n",
            "reading /usr/share/mecab/dic/ipadic/Adnominal.csv ... 135\n",
            "emitting double-array: 100% |###########################################| \n",
            "reading /usr/share/mecab/dic/ipadic/matrix.def ... 1316x1316\n",
            "emitting matrix      : 100% |###########################################| \n",
            "\n",
            "done!\n",
            "update-alternatives: using /var/lib/mecab/dic/ipadic-utf8 to provide /var/lib/mecab/dic/debian (mecab-dictionary) in auto mode\n",
            "Setting up libhtml-parser-perl:amd64 (3.76-1build2) ...\n",
            "Setting up mysql-server (8.0.40-0ubuntu0.22.04.1) ...\n",
            "Setting up libcgi-pm-perl (4.54-1) ...\n",
            "Setting up libhtml-template-perl (2.97-1.1) ...\n",
            "Setting up libcgi-fast-perl (1:2.15-1) ...\n",
            "Processing triggers for man-db (2.10.2-1) ...\n",
            "Processing triggers for libc-bin (2.35-0ubuntu3.4) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtcm_debug.so.1 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libur_loader.so.0 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libhwloc.so.15 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libumf.so.0 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtcm.so.1 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libur_adapter_level_zero.so.0 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libur_adapter_opencl.so.0 is not a symbolic link\n",
            "\n",
            " * Starting MySQL database server mysqld\n",
            "su: warning: cannot change directory to /nonexistent: No such file or directory\n",
            "   ...done.\n"
          ]
        }
      ],
      "source": [
        "# mysql을 설치하고 데이터를 로드한 다음 접속 권한을 설정합니다.\n",
        "!apt-get -qq -y install mysql-server\n",
        "!printf \"[mysqld]\\nsecure-file-priv = ''\" >> /etc/alternatives/my.cnf\n",
        "!service mysql start\n",
        "!wget -q https://bit.ly/47Hl2t5 -O data.csv\n",
        "!wget -q https://bit.ly/4c42MxF -O setup.sql\n",
        "!mysql < 'setup.sql'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "id": "s6Tcz-KNXPnD",
        "outputId": "959b1902-dfac-4108-d1f4-4ded806817b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-23-7e710944fb18>:14: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
            "  dataframe = pd.read_sql(\"select * from data\", conn)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   integer            datetime  category\n",
              "0        5 2015-01-01 00:00:00         0\n",
              "1        5 2015-01-01 00:00:01         0"
            ],
            "text/html": [
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      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-80e7cdde-e908-44d5-821b-79af1b7ca742 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-80e7cdde-e908-44d5-821b-79af1b7ca742');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --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",
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              "    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-a86dad22-d4c7-4f4b-bdbc-39a7c5805995 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataframe",
              "summary": "{\n  \"name\": \"dataframe\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"integer\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          4,\n          9,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"datetime\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2015-01-01 00:00:00\",\n        \"max\": \"2015-01-01 00:01:39\",\n        \"num_unique_values\": 100,\n        \"samples\": [\n          \"2015-01-01 00:01:23\",\n          \"2015-01-01 00:00:53\",\n          \"2015-01-01 00:01:10\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "# 라이브러리를 임포트합니다.\n",
        "import pymysql\n",
        "import pandas as pd\n",
        "\n",
        "# 데이터베이스를 연결합니다.\n",
        "conn = pymysql.connect(\n",
        "    host='localhost',\n",
        "    user='root',\n",
        "    password='root',\n",
        "    db='db',\n",
        ")\n",
        "\n",
        "# 데이터프레임으로 데이터를 읽습니다.\n",
        "dataframe = pd.read_sql(\"select * from data\", conn)\n",
        "\n",
        "# 처음 두 개의 행을 출력합니다.\n",
        "dataframe.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.10 구글 시트에서 데이터 적재하기"
      ],
      "metadata": {
        "id": "T0pO7zvI0Px6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 라이브러리를 임포트합니다.\n",
        "import pandas as pd\n",
        "\n",
        "# 구글 시트를 CSV로 다운로드하는 URL\n",
        "url = \"https://bit.ly/3SQU24Y\"\n",
        "\n",
        "# CSV를 데이터프레임으로 읽스니다.\n",
        "dataframe = pd.read_csv(url)\n",
        "\n",
        "# 처음 두 개의 행을 출력합니다.\n",
        "dataframe.head(2)"
      ],
      "metadata": {
        "id": "DyJxQpCI0Pge",
        "outputId": "58ae7b91-f482-4cd5-b1b8-f1fecda7248d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        }
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   integer            datetime  category\n",
              "0        5  2015-01-01 0:00:00         0\n",
              "1        5  2015-01-01 0:00:01         0"
            ],
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>category</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>0</td>\n",
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              "      <th>1</th>\n",
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              "      <td>2015-01-01 0:00:01</td>\n",
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              "    </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",
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              "    }\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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              "\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ecb078ed-09fc-488a-b4ed-658ed0b29956 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-ecb078ed-09fc-488a-b4ed-658ed0b29956');\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-02944d78-d99d-4133-a3c1-43b572cd1c6d')\"\n",
              "            title=\"Suggest charts\"\n",
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              "\n",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-02944d78-d99d-4133-a3c1-43b572cd1c6d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataframe",
              "summary": "{\n  \"name\": \"dataframe\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"integer\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          4,\n          9,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"datetime\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 100,\n        \"samples\": [\n          \"2015-01-01 0:01:23\",\n          \"2015-01-01 0:00:53\",\n          \"2015-01-01 0:01:10\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.12 비구조적인 데이터 적재하기"
      ],
      "metadata": {
        "id": "LtkSScgB0ozV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 라이브러리를 임포트합니다.\n",
        "import requests\n",
        "\n",
        "# 텍스트 파일을 다운로드할 URL\n",
        "txt_url = \"https://bit.ly/3SVj6YI\"\n",
        "\n",
        "# 텍스트 파일을 다운로드합니다.\n",
        "r = requests.get(txt_url)\n",
        "\n",
        "# 로컬 text.txt 파일에 씁니다.\n",
        "with open('text.txt', 'wb') as f:\n",
        "    f.write(r.content)\n",
        "\n",
        "# 파일을 읽습니다.\n",
        "with open('text.txt', 'r') as f:\n",
        "    text = f.read()\n",
        "\n",
        "# 내용을 출력합니다.\n",
        "print(text)"
      ],
      "metadata": {
        "id": "sKr4CySy0syd",
        "outputId": "523f6b99-9d92-4292-c82b-bf28768b29ed",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hello there!\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.13"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}