Day3판다스데이터분석_03데이터살펴보기.ipynb

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        "<a href=\"https://colab.research.google.com/github/wnsmir/DSBA5/blob/main/Day3%E1%84%91%E1%85%A1%E1%86%AB%E1%84%83%E1%85%A1%E1%84%89%E1%85%B3%E1%84%83%E1%85%A6%E1%84%8B%E1%85%B5%E1%84%90%E1%85%A5%E1%84%87%E1%85%AE%E1%86%AB%E1%84%89%E1%85%A5%E1%86%A8_03%E1%84%83%E1%85%A6%E1%84%8B%E1%85%B5%E1%84%90%E1%85%A5%E1%84%89%E1%85%A1%E1%86%AF%E1%84%91%E1%85%A7%E1%84%87%E1%85%A9%E1%84%80%E1%85%B5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
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        "id": "uQTbKndXmtvj"
      },
      "source": [
        "# **03 데이터 살펴보기**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xulRdkMVeFAw"
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        "# 1. 데이터프레임의 구조\n",
        "* 자동차 연비(auto mpg) 데이터셋\n",
        "* [표] UCI 데이터셋- \"auto mpg\" 상세 항목\n",
        "\n",
        "|No|속성(attributes)|데이터 상세(범위)|\n",
        "|------|---|---|\n",
        "|1|mpg(연비)|연속값|\n",
        "|2|cylinders(실린더 수)|이산값(예: 3, 4, 6, 8)|\n",
        "|3|displacement(배기량)|연속값|\n",
        "|4|horsepower(출력)|연속값|\n",
        "|5|weight(차종)|연속값|\n",
        "|6|acceleration(가속능력)|연속값|\n",
        "|7|model_year(출시년도)|이산값(예: 70, 71, 80, 81)|\n",
        "|8|orgin(제조국)|이산값(예: 1(USA), 2(EU), 3(JPN)|\n",
        "|9|name(모델명)|문자열|\n",
        "\n",
        "\n",
        "## 1-1 데이터 내용 미리보기\n",
        "\n",
        "```\n",
        "* 앞부분 미리보기: DataFrame 객체.head(n)\n",
        "* 뒷부분 미리보기: DataFrame 객체.tail(n)\n",
        "```\n",
        "* 여기서 정수 n을 입력하지 않으면 5개의 행을 보여준다,(디폴트값: n=5)\n"
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          "text": [
            "Mounted at /content/drive\n"
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      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
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              "      0  1      2      3       4     5   6  7                          8\n",
              "0  18.0  8  307.0  130.0  3504.0  12.0  70  1  chevrolet chevelle malibu\n",
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              "3  16.0  8  304.0  150.0  3433.0  12.0  70  1              amc rebel sst\n",
              "4  17.0  8  302.0  140.0  3449.0  10.5  70  1                ford torino"
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      "source": [
        "file_path = '/content/drive/MyDrive/DSBA파이썬판다스데이터분석/3장자료/auto-mpg.csv'\n",
        "import pandas as pd\n",
        "df = pd.read_csv(file_path, header=None)\n",
        "df.head()"
      ]
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 398,\n  \"fields\": [\n    {\n      \"column\": \"mpg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.815984312565782,\n        \"min\": 9.0,\n        \"max\": 46.6,\n        \"num_unique_values\": 129,\n        \"samples\": [\n          17.7,\n          30.5,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cylinders\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 3,\n        \"max\": 8,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4,\n          5,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"displacement\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 104.26983817119591,\n        \"min\": 68.0,\n        \"max\": 455.0,\n        \"num_unique_values\": 82,\n        \"samples\": [\n          122.0,\n          307.0,\n          360.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"horsepower\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 94,\n        \"samples\": [\n          \"112.0\",\n          \"?\",\n          \"78.00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 846.8417741973268,\n        \"min\": 1613.0,\n        \"max\": 5140.0,\n        \"num_unique_values\": 351,\n        \"samples\": [\n          3730.0,\n          1995.0,\n          2215.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"acceleration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.757688929812676,\n        \"min\": 8.0,\n        \"max\": 24.8,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          14.7,\n          18.0,\n          14.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 70,\n        \"max\": 82,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          81,\n          79,\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"origin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 305,\n        \"samples\": [\n          \"mazda rx-4\",\n          \"ford f108\",\n          \"buick century luxus (sw)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mpg</th>\n",
              "      <th>cylinders</th>\n",
              "      <th>displacement</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>weight</th>\n",
              "      <th>acceleration</th>\n",
              "      <th>model year</th>\n",
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              "      <th>name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>18.0</td>\n",
              "      <td>8</td>\n",
              "      <td>307.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>3504.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>chevrolet chevelle malibu</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15.0</td>\n",
              "      <td>8</td>\n",
              "      <td>350.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>3693.0</td>\n",
              "      <td>11.5</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>buick skylark 320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>18.0</td>\n",
              "      <td>8</td>\n",
              "      <td>318.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>3436.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>plymouth satellite</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16.0</td>\n",
              "      <td>8</td>\n",
              "      <td>304.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>3433.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>amc rebel sst</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17.0</td>\n",
              "      <td>8</td>\n",
              "      <td>302.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>3449.0</td>\n",
              "      <td>10.5</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>ford torino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>27.0</td>\n",
              "      <td>4</td>\n",
              "      <td>140.0</td>\n",
              "      <td>86.00</td>\n",
              "      <td>2790.0</td>\n",
              "      <td>15.6</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "      <td>ford mustang gl</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>44.0</td>\n",
              "      <td>4</td>\n",
              "      <td>97.0</td>\n",
              "      <td>52.00</td>\n",
              "      <td>2130.0</td>\n",
              "      <td>24.6</td>\n",
              "      <td>82</td>\n",
              "      <td>2</td>\n",
              "      <td>vw pickup</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>395</th>\n",
              "      <td>32.0</td>\n",
              "      <td>4</td>\n",
              "      <td>135.0</td>\n",
              "      <td>84.00</td>\n",
              "      <td>2295.0</td>\n",
              "      <td>11.6</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "      <td>dodge rampage</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>28.0</td>\n",
              "      <td>4</td>\n",
              "      <td>120.0</td>\n",
              "      <td>79.00</td>\n",
              "      <td>2625.0</td>\n",
              "      <td>18.6</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "      <td>ford ranger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>31.0</td>\n",
              "      <td>4</td>\n",
              "      <td>119.0</td>\n",
              "      <td>82.00</td>\n",
              "      <td>2720.0</td>\n",
              "      <td>19.4</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "      <td>chevy s-10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>398 rows × 9 columns</p>\n",
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              "\n",
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            ],
            "text/plain": [
              "      mpg  cylinders  displacement horsepower  weight  acceleration  \\\n",
              "0    18.0          8         307.0      130.0  3504.0          12.0   \n",
              "1    15.0          8         350.0      165.0  3693.0          11.5   \n",
              "2    18.0          8         318.0      150.0  3436.0          11.0   \n",
              "3    16.0          8         304.0      150.0  3433.0          12.0   \n",
              "4    17.0          8         302.0      140.0  3449.0          10.5   \n",
              "..    ...        ...           ...        ...     ...           ...   \n",
              "393  27.0          4         140.0      86.00  2790.0          15.6   \n",
              "394  44.0          4          97.0      52.00  2130.0          24.6   \n",
              "395  32.0          4         135.0      84.00  2295.0          11.6   \n",
              "396  28.0          4         120.0      79.00  2625.0          18.6   \n",
              "397  31.0          4         119.0      82.00  2720.0          19.4   \n",
              "\n",
              "     model year  origin                       name  \n",
              "0            70       1  chevrolet chevelle malibu  \n",
              "1            70       1          buick skylark 320  \n",
              "2            70       1         plymouth satellite  \n",
              "3            70       1              amc rebel sst  \n",
              "4            70       1                ford torino  \n",
              "..          ...     ...                        ...  \n",
              "393          82       1            ford mustang gl  \n",
              "394          82       2                  vw pickup  \n",
              "395          82       1              dodge rampage  \n",
              "396          82       1                ford ranger  \n",
              "397          82       1                 chevy s-10  \n",
              "\n",
              "[398 rows x 9 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.columns = ['mpg','cylinders','displacement','horsepower','weight',\n",
        "              'acceleration','model year','origin','name']\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BJW1HBe_c0Zi",
        "outputId": "5d147c30-1807-4823-d738-932104737f51"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "    mpg  cylinders  displacement horsepower  weight  acceleration  model year  \\\n",
            "0  18.0          8         307.0      130.0  3504.0          12.0          70   \n",
            "1  15.0          8         350.0      165.0  3693.0          11.5          70   \n",
            "2  18.0          8         318.0      150.0  3436.0          11.0          70   \n",
            "3  16.0          8         304.0      150.0  3433.0          12.0          70   \n",
            "4  17.0          8         302.0      140.0  3449.0          10.5          70   \n",
            "\n",
            "   origin                       name  \n",
            "0       1  chevrolet chevelle malibu  \n",
            "1       1          buick skylark 320  \n",
            "2       1         plymouth satellite  \n",
            "3       1              amc rebel sst  \n",
            "4       1                ford torino  \n",
            "\n",
            "\n",
            "      mpg  cylinders  displacement horsepower  weight  acceleration  \\\n",
            "393  27.0          4         140.0      86.00  2790.0          15.6   \n",
            "394  44.0          4          97.0      52.00  2130.0          24.6   \n",
            "395  32.0          4         135.0      84.00  2295.0          11.6   \n",
            "396  28.0          4         120.0      79.00  2625.0          18.6   \n",
            "397  31.0          4         119.0      82.00  2720.0          19.4   \n",
            "\n",
            "     model year  origin             name  \n",
            "393          82       1  ford mustang gl  \n",
            "394          82       2        vw pickup  \n",
            "395          82       1    dodge rampage  \n",
            "396          82       1      ford ranger  \n",
            "397          82       1       chevy s-10  \n"
          ]
        }
      ],
      "source": [
        "# <예제 3-1> 데이터 살펴보기\n",
        "# -*- coding: utf-8 -*-\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "# read_csv() 함수로 df 생성\n",
        "df = pd.read_csv('./auto-mpg.csv', header=None)\n",
        "\n",
        "# 열 이름을 지정\n",
        "df.columns = ['mpg','cylinders','displacement','horsepower','weight',\n",
        "              'acceleration','model year','origin','name']\n",
        "\n",
        "# 데이터프레임 df의 내용을 일부 확인\n",
        "print(df.head())     # 처음 5개의 행\n",
        "print('\\n')\n",
        "print(df.tail())     # 마지막 5개의 행"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lPFH0jD9d_wx"
      },
      "source": [
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GhmE4ljldGV8"
      },
      "source": [
        "## 1-2 데이터 요약 정보 확인하기\n",
        "### 1. 데이터프레임의 크기(행, 열)\n",
        "\n",
        "```\n",
        "데이터프레임의 크기 확인: DataFrame 객체.shape\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1P12vunNeEx9",
        "outputId": "5d583684-7cf8-4e48-c31a-1cb4d6b7e92e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(398, 9)\n"
          ]
        }
      ],
      "source": [
        "# df의 모양과 크기 확인: (행의 개수, 열의 개수)를 투플로 반환\n",
        "print(df.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZKv0a4RieYBJ"
      },
      "source": [
        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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xAeUaRoadLsr"
      },
      "source": [
        "### 2. 데이터프레임의 기본 정보\n",
        "\n",
        "```\n",
        "데이터프레임의 기본 정보 출력: DataFrame 객체.info()\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tiu6CfVlecyS",
        "outputId": "eebeb8f1-6661-4f3d-fc68-6b89cae67c3c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 398 entries, 0 to 397\n",
            "Data columns (total 9 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   mpg           398 non-null    float64\n",
            " 1   cylinders     398 non-null    int64  \n",
            " 2   displacement  398 non-null    float64\n",
            " 3   horsepower    398 non-null    object \n",
            " 4   weight        398 non-null    float64\n",
            " 5   acceleration  398 non-null    float64\n",
            " 6   model year    398 non-null    int64  \n",
            " 7   origin        398 non-null    int64  \n",
            " 8   name          398 non-null    object \n",
            "dtypes: float64(4), int64(3), object(2)\n",
            "memory usage: 28.1+ KB\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "# 데이터프레임 df의 내용 확인\n",
        "print(df.info())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JGeiQULhdQoY"
      },
      "source": [
        "* [참고] 판다스 자료형의 종류\n",
        "\n",
        "|판다스 자료형|파이썬 자료형|비고|\n",
        "|------|---|---|\n",
        "|int64|int|정수형 데이터|\n",
        "|flote64|cylfloat|실수형 데이터(소수점이 있는 경우)|\n",
        "|object|string|문자형 데이터|\n",
        "|datetime64, timedelta64|없음(datetime 라이브러리 활용)|시간 데이터|\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lsykPv-qgBqN",
        "outputId": "4d8d1dd7-7c31-4a1b-82af-9bf310beead6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mpg             float64\n",
            "cylinders         int64\n",
            "displacement    float64\n",
            "horsepower       object\n",
            "weight          float64\n",
            "acceleration    float64\n",
            "model year        int64\n",
            "origin            int64\n",
            "name             object\n",
            "dtype: object\n",
            "\n",
            "\n",
            "float64\n"
          ]
        }
      ],
      "source": [
        "# 데이터프레임 df의 자료형 확인\n",
        "print(df.dtypes)\n",
        "print('\\n')\n",
        "\n",
        "# 시리즈(mog 열)의 자료형 확인\n",
        "print(df.mpg.dtypes)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y3l3be-9ePcQ"
      },
      "source": [
        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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "miIAsyUSdX3a"
      },
      "source": [
        "### 3. 데이터프레임의 기술 통계 정보 요약\n",
        "\n",
        "```\n",
        "데이터프레임의 기술 통계 정보 요약: DataFrame 객체.describe()\n",
        "```\n",
        "* 산술 데이터가 아닌 열에 대한 정보를 포함하고 싶을 때는 include='all' 옵션을 추가"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ScPe001EgxzO",
        "outputId": "e41dcf63-7477-4afa-c045-b89799d0b5b0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              mpg   cylinders  displacement       weight  acceleration  \\\n",
            "count  398.000000  398.000000    398.000000   398.000000    398.000000   \n",
            "mean    23.514573    5.454774    193.425879  2970.424623     15.568090   \n",
            "std      7.815984    1.701004    104.269838   846.841774      2.757689   \n",
            "min      9.000000    3.000000     68.000000  1613.000000      8.000000   \n",
            "25%     17.500000    4.000000    104.250000  2223.750000     13.825000   \n",
            "50%     23.000000    4.000000    148.500000  2803.500000     15.500000   \n",
            "75%     29.000000    8.000000    262.000000  3608.000000     17.175000   \n",
            "max     46.600000    8.000000    455.000000  5140.000000     24.800000   \n",
            "\n",
            "       model year      origin  \n",
            "count  398.000000  398.000000  \n",
            "mean    76.010050    1.572864  \n",
            "std      3.697627    0.802055  \n",
            "min     70.000000    1.000000  \n",
            "25%     73.000000    1.000000  \n",
            "50%     76.000000    1.000000  \n",
            "75%     79.000000    2.000000  \n",
            "max     82.000000    3.000000  \n",
            "\n",
            "\n",
            "               mpg   cylinders  displacement horsepower       weight  \\\n",
            "count   398.000000  398.000000    398.000000        398   398.000000   \n",
            "unique         NaN         NaN           NaN         94          NaN   \n",
            "top            NaN         NaN           NaN      150.0          NaN   \n",
            "freq           NaN         NaN           NaN         22          NaN   \n",
            "mean     23.514573    5.454774    193.425879        NaN  2970.424623   \n",
            "std       7.815984    1.701004    104.269838        NaN   846.841774   \n",
            "min       9.000000    3.000000     68.000000        NaN  1613.000000   \n",
            "25%      17.500000    4.000000    104.250000        NaN  2223.750000   \n",
            "50%      23.000000    4.000000    148.500000        NaN  2803.500000   \n",
            "75%      29.000000    8.000000    262.000000        NaN  3608.000000   \n",
            "max      46.600000    8.000000    455.000000        NaN  5140.000000   \n",
            "\n",
            "        acceleration  model year      origin        name  \n",
            "count     398.000000  398.000000  398.000000         398  \n",
            "unique           NaN         NaN         NaN         305  \n",
            "top              NaN         NaN         NaN  ford pinto  \n",
            "freq             NaN         NaN         NaN           6  \n",
            "mean       15.568090   76.010050    1.572864         NaN  \n",
            "std         2.757689    3.697627    0.802055         NaN  \n",
            "min         8.000000   70.000000    1.000000         NaN  \n",
            "25%        13.825000   73.000000    1.000000         NaN  \n",
            "50%        15.500000   76.000000    1.000000         NaN  \n",
            "75%        17.175000   79.000000    2.000000         NaN  \n",
            "max        24.800000   82.000000    3.000000         NaN  \n"
          ]
        }
      ],
      "source": [
        "# 데이터프레임 df의 기술통계 정보 확인\n",
        "print(df.describe())\n",
        "print('\\n')\n",
        "print(df.describe(include='all'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "llfGYolQdapL"
      },
      "source": [
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TjDvkgv2ecH_"
      },
      "source": [
        "## 1-3 데이터 개수 확인\n",
        "### 1. 각 열의 데이터 개수\n",
        "\n",
        "```\n",
        "열 데이터 개수 확인: DataFrame 객체.count()\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4CWibC5NV8_E",
        "outputId": "1f353ea1-ec6c-4a8a-d80d-7121beaa46af"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mpg             398\n",
            "cylinders       398\n",
            "displacement    398\n",
            "horsepower      398\n",
            "weight          398\n",
            "acceleration    398\n",
            "model year      398\n",
            "origin          398\n",
            "name            398\n",
            "dtype: int64\n",
            "\n",
            "\n",
            "<class 'pandas.core.series.Series'>\n"
          ]
        }
      ],
      "source": [
        "# <예제 3-2> 데이터 개수 확인\n",
        "# -*- coding: utf-8 -*-\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "# read_csv() 함수로 df 생성\n",
        "df = pd.read_csv('./auto-mpg.csv', header=None)\n",
        "\n",
        "# 열 이름을 지정\n",
        "df.columns = ['mpg','cylinders','displacement','horsepower','weight',\n",
        "              'acceleration','model year','origin','name']\n",
        "\n",
        "# 데이터프레임 df의 각 열이 가지고 있는 원소 개수 확인\n",
        "print(df.count())\n",
        "print('\\n')\n",
        "\n",
        "# df.count()가 반환하는 객체 타입 출력\n",
        "print(type(df.count()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lTWauLjiefDL"
      },
      "source": [
        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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aVW0q3FNewQ6"
      },
      "source": [
        "### 2. 각 열의 고윳값 개수\n",
        "\n",
        "```\n",
        "열 데이터의 고윳값 개수: DataFrame 객체[\"열 이름\"].value_count()\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9YK_e2kUYYrK",
        "outputId": "abc06dfc-1d01-486c-c0df-d4dc2b3d5519"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1    249\n",
            "3     79\n",
            "2     70\n",
            "Name: origin, dtype: int64\n",
            "\n",
            "\n",
            "<class 'pandas.core.series.Series'>\n"
          ]
        }
      ],
      "source": [
        "# 데이터프레임 df의 특정 열이 가지고 있는 고유값 확인\n",
        "unique_values = df['origin'].value_counts()\n",
        "print(unique_values)\n",
        "print('\\n')\n",
        "\n",
        "# value_counts 메소드가 반환하는 객체 타입 출력\n",
        "print(type(unique_values))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6qcfnoLYezbL"
      },
      "source": [
        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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VV7TP0IUe-3n"
      },
      "source": [
        "# 2. 통계 함수 적용\n",
        "## 2-1 평균값\n",
        "\n",
        "```\n",
        "* 모든 열의 평균값: DataFrame 객체.mean()\n",
        "* 특정 열의 평균값: DataFrame 객체[\"열 이름\"].mean()\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GkRqs27KZGk-",
        "outputId": "a82c806b-7920-4b3e-f997-93a9c5c1df69"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mpg               23.514573\n",
            "cylinders          5.454774\n",
            "displacement     193.425879\n",
            "weight          2970.424623\n",
            "acceleration      15.568090\n",
            "model year        76.010050\n",
            "origin             1.572864\n",
            "dtype: float64\n",
            "\n",
            "\n",
            "23.514572864321607\n",
            "23.514572864321607\n",
            "\n",
            "\n",
            "mpg         23.514573\n",
            "weight    2970.424623\n",
            "dtype: float64\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-3-3a1e9d19d949>:14: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  print(df.mean())\n"
          ]
        }
      ],
      "source": [
        "# <예제 3-3> 통계 함수\n",
        "# -*- coding: utf-8 -*-\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "# read_csv() 함수로 df 생성\n",
        "df = pd.read_csv('./auto-mpg.csv', header=None)\n",
        "\n",
        "# 열 이름을 지정\n",
        "df.columns = ['mpg','cylinders','displacement','horsepower','weight',\n",
        "              'acceleration','model year','origin','name']\n",
        "\n",
        "# 평균값\n",
        "print(df.mean())\n",
        "print('\\n')\n",
        "print(df['mpg'].mean())\n",
        "print(df.mpg.mean())\n",
        "print('\\n')\n",
        "print(df[['mpg','weight']].mean())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CcDHNGxTe0ym"
      },
      "source": [
        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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bktCKXtZfCZ5"
      },
      "source": [
        "## 2-2 중간값\n",
        "\n",
        "```\n",
        "* 모든 열의 중간값: DataFrame 객체.median()\n",
        "* 특정 열의 중간값: DataFrame 객체[\"열 이름\"].median()\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R8PhqXZtZ3Vz",
        "outputId": "b846ed48-27a9-4cb3-cf7a-8f41268bd664"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mpg               23.0\n",
            "cylinders          4.0\n",
            "displacement     148.5\n",
            "weight          2803.5\n",
            "acceleration      15.5\n",
            "model year        76.0\n",
            "origin             1.0\n",
            "dtype: float64\n",
            "\n",
            "\n",
            "23.0\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-4-392f16fcc621>:2: FutureWarning: The default value of numeric_only in DataFrame.median is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  print(df.median())\n"
          ]
        }
      ],
      "source": [
        "# 중간값\n",
        "print(df.median())\n",
        "print('\\n')\n",
        "print(df['mpg'].median())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cHcIXP5pe2HG"
      },
      "source": [
        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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IVHp_rCjdf8b"
      },
      "source": [
        "## 2-3 최댓값\n",
        "\n",
        "```\n",
        "* 모든 열의 최댓값: DataFrame 객체.max()\n",
        "* 특정 열의 최댓값: DataFrame 객체[\"열 이름\"].max()\n",
        "```\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oahqP4tLaJhn",
        "outputId": "504ab131-ae7d-40b5-fffc-5bd54a31c411"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mpg                         46.6\n",
            "cylinders                      8\n",
            "displacement               455.0\n",
            "horsepower                     ?\n",
            "weight                    5140.0\n",
            "acceleration                24.8\n",
            "model year                    82\n",
            "origin                         3\n",
            "name            vw rabbit custom\n",
            "dtype: object\n",
            "\n",
            "\n",
            "46.6\n"
          ]
        }
      ],
      "source": [
        "# 최댓값\n",
        "print(df.max())\n",
        "print('\\n')\n",
        "print(df['mpg'].max())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S6sEA43Pe3Zl"
      },
      "source": [
        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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yj9VBJiqfG9W"
      },
      "source": [
        "## 2-4 최솟값\n",
        "\n",
        "```\n",
        "* 모든 열의 최솟값: DataFrame 객체.min()\n",
        "* 특정 열의 최솟값: DataFrame 객체[\"열 이름\"].min()\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4RPV74ataZcv",
        "outputId": "2d738701-5df8-475c-e9be-0692c3bd85c7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mpg                                 9.0\n",
            "cylinders                             3\n",
            "displacement                       68.0\n",
            "horsepower                        100.0\n",
            "weight                           1613.0\n",
            "acceleration                        8.0\n",
            "model year                           70\n",
            "origin                                1\n",
            "name            amc ambassador brougham\n",
            "dtype: object\n",
            "\n",
            "\n",
            "9.0\n"
          ]
        }
      ],
      "source": [
        "# 최솟값\n",
        "print(df.min())\n",
        "print('\\n')\n",
        "print(df['mpg'].min())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MpKtMEcRe4ya"
      },
      "source": [
        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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V8PcsxeRfKz4"
      },
      "source": [
        "## 2-5 표준편차\n",
        "\n",
        "```\n",
        "* 모든 열의 표준편차: DataFrame 객체.std()\n",
        "* 특정 열의 표준편차: DataFrame 객체[\"열 이름\"].std()\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ntYX_j2axMF",
        "outputId": "93bc78e8-7fc7-47ce-f766-df296fb37db3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mpg               7.815984\n",
            "cylinders         1.701004\n",
            "displacement    104.269838\n",
            "weight          846.841774\n",
            "acceleration      2.757689\n",
            "model year        3.697627\n",
            "origin            0.802055\n",
            "dtype: float64\n",
            "\n",
            "\n",
            "7.815984312565782\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-7-edd9452e1c33>:2: FutureWarning: The default value of numeric_only in DataFrame.std is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  print(df.std())\n"
          ]
        }
      ],
      "source": [
        "# 표준편차\n",
        "print(df.std())\n",
        "print('\\n')\n",
        "print(df['mpg'].std())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2gCmB3vEfOYi"
      },
      "source": [
        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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QGNWj-w-fZZg"
      },
      "source": [
        "## 2-6 상관계수\n",
        "\n",
        "```\n",
        "* 모든 열의 상관계수: DataFrame 객체.corr()\n",
        "* 특정 열의 상관계수: DataFrame 객체[\"열 이름\"].corr()\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "badtIMVfbHlo",
        "outputId": "a5b91bac-57f9-4685-a63f-9edeb1c2aade"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   mpg  cylinders  displacement    weight  acceleration  \\\n",
            "mpg           1.000000  -0.775396     -0.804203 -0.831741      0.420289   \n",
            "cylinders    -0.775396   1.000000      0.950721  0.896017     -0.505419   \n",
            "displacement -0.804203   0.950721      1.000000  0.932824     -0.543684   \n",
            "weight       -0.831741   0.896017      0.932824  1.000000     -0.417457   \n",
            "acceleration  0.420289  -0.505419     -0.543684 -0.417457      1.000000   \n",
            "model year    0.579267  -0.348746     -0.370164 -0.306564      0.288137   \n",
            "origin        0.563450  -0.562543     -0.609409 -0.581024      0.205873   \n",
            "\n",
            "              model year    origin  \n",
            "mpg             0.579267  0.563450  \n",
            "cylinders      -0.348746 -0.562543  \n",
            "displacement   -0.370164 -0.609409  \n",
            "weight         -0.306564 -0.581024  \n",
            "acceleration    0.288137  0.205873  \n",
            "model year      1.000000  0.180662  \n",
            "origin          0.180662  1.000000  \n",
            "\n",
            "\n",
            "             mpg    weight\n",
            "mpg     1.000000 -0.831741\n",
            "weight -0.831741  1.000000\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-8-31b9f460ef17>:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  print(df.corr())\n"
          ]
        }
      ],
      "source": [
        "# 상관계수\n",
        "print(df.corr())\n",
        "print('\\n')\n",
        "print(df[['mpg','weight']].corr())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cj5hbkQufPzn"
      },
      "source": [
        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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xk_KBLJSfTUX"
      },
      "source": [
        "# 3. 판다스 내장 그래프 도구 활용\n",
        "* [표] 판다스 내장 plot() 메소드 - 그래프 종류\n",
        "\n",
        "|kind 옵션|설명|kind 옵션|설명|\n",
        "|------|---|---|---|\n",
        "|'line'|선 그래프|'kde'|커널 밀도 그래프|\n",
        "|'bar'|수직 막대 그래프|'area'|면적 그래프|\n",
        "|'barh'|수평 막대 그래프|'pie'|파이 그래프|\n",
        "|'his'|히스토그램|'scatter'|산점도 그래프|\n",
        "|'box'|박스플롯|'hexbin'|고밀도 산점도 그래프|\n",
        "\n",
        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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hJi96dSKfjdl"
      },
      "source": [
        "### 1. 선 그래프\n",
        "\n",
        "```\n",
        "선 그래프: DataFrame 객체.plot()\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 837
        },
        "id": "TzFUMYcZd9FU",
        "outputId": "67726eba-27a1-4825-d211-707ccde50a68"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "       1991  1992  1993  1994  1995  1996  1997  1998  1999  2000  ...  2007  \\\n",
            "South  1186  1310  1444  1650  1847  2055  2244  2153  2393  2664  ...  4031   \n",
            "North   263   247   221   231   230   213   193   170   186   194  ...   236   \n",
            "\n",
            "       2008  2009  2010  2011  2012  2013  2014  2015  2016  \n",
            "South  4224  4336  4747  4969  5096  5171  5220  5281  5404  \n",
            "North   255   235   237   211   215   221   216   190   239  \n",
            "\n",
            "[2 rows x 26 columns]\n",
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# <예제 3-4> 선 그래프 그리기\n",
        "# -*- coding: utf-8 -*-\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "df = pd.read_excel('./남북한발전전력량.xlsx', engine='openpyxl')  # 데이터프레임 변환\n",
        "\n",
        "df_ns = df.iloc[[0, 5], 3:]            # 남한, 북한 발전량 합계 데이터만 추출\n",
        "df_ns.index = ['South','North']        # 행 인덱스 변경\n",
        "df_ns.columns = df_ns.columns.map(int) # 열 이름의 자료형을 정수형으로 변경\n",
        "print(df_ns.head())\n",
        "print('\\n')\n",
        "\n",
        "# 선 그래프 그리기\n",
        "df_ns.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jGEwgm_ofnwZ"
      },
      "source": [
        "* 행 인덱스(df_ns의 행 인덱스;'South','North')를 x축 데이터로 적용\n",
        "* 시간의 흐름에 따른 연도별 발전량 변화 추이를 보기 위해\n",
        "  * 연도 값을 x축으로 표시(행, 열 전치)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        },
        "id": "DIX9QI_NeOpf",
        "outputId": "bfcc3998-041e-4cfa-d0a0-e140e29382c1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     South North\n",
            "1991  1186   263\n",
            "1992  1310   247\n",
            "1993  1444   221\n",
            "1994  1650   231\n",
            "1995  1847   230\n",
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# 행, 열 전치하여 다시 그리기\n",
        "tdf_ns = df_ns.T\n",
        "print(tdf_ns.head())\n",
        "print('\\n')\n",
        "tdf_ns.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FiO4MMgHfp9n"
      },
      "source": [
        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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "95LvSKVNfvfT"
      },
      "source": [
        "### 2. 막대 그래프\n",
        "\n",
        "```\n",
        "막대 그래프: DataFrame 객체.plot(kind='bar')\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "9sQsVPXqgLxK",
        "outputId": "f8a66f04-7fcb-45bc-a7b1-0b31c7ccdf9d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     South North\n",
            "1991  1186   263\n",
            "1992  1310   247\n",
            "1993  1444   221\n",
            "1994  1650   231\n",
            "1995  1847   230\n",
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# <예제 3-5> 막대 그래프\n",
        "# -*- coding: utf-8 -*-\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "df = pd.read_excel('./남북한발전전력량.xlsx', engine='openpyxl')  # 데이터프레임 변환\n",
        "\n",
        "df_ns = df.iloc[[0, 5], 3:]            # 남한, 북한 발전량 합계 데이터만 추출\n",
        "df_ns.index = ['South','North']        # 행 인덱스 변경\n",
        "df_ns.columns = df_ns.columns.map(int) # 열 이름의 자료형을 정수형으로 변경\n",
        "\n",
        "# 행, 열 전치하여 막대 그래프 그리기\n",
        "tdf_ns = df_ns.T\n",
        "print(tdf_ns.head())\n",
        "print('\\n')\n",
        "tdf_ns.plot(kind='bar')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dz0TICvqfy0V"
      },
      "source": [
        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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7xPa8bXhgDfK"
      },
      "source": [
        "### 3. 히스토그램\n",
        "\n",
        "```\n",
        "히스토그램: DataFrame 객체.plot(kind='hist')\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oOKZBcwWiGJL"
      },
      "outputs": [],
      "source": [
        "# colab에서 히스토그램 에러(Jupyter Notebook에서 run한 결과)\n",
        "# <예제 3-6> 히스토그램\n",
        "# -*- coding: utf-8 -*-\n",
        "\n",
        "#import pandas as pd\n",
        "\n",
        "#df = pd.read_excel('./남북한발전전력량.xlsx', engine='openpyxl')  # 데이터프레임 변환\n",
        "\n",
        "#df_ns = df.iloc[[0, 5], 3:]            # 남한, 북한 발전량 합계 데이터만 추출\n",
        "#df_ns.index = ['South','North']        # 행 인덱스 변경\n",
        "#df_ns.columns = df_ns.columns.map(int) # 열 이름의 자료형을 정수형으로 변경\n",
        "\n",
        "# 행, 열 전치하여 히스토그램 그리기\n",
        "#tdf_ns = df_ns.T\n",
        "#tdf_ns.plot(kind='hist')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MIjP4yofiGyh"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MHd-0qcTf0aK"
      },
      "source": [
        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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jG5oG4_uf_aV"
      },
      "source": [
        "### 4. 산점도\n",
        "\n",
        "```\n",
        "산점도: DataFrame 객체.plot(kind='scatter')\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "SjIeR4ILkHHL",
        "outputId": "1c903e2e-deb1-4205-ccab-dfdf0d4e2f10"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='weight', ylabel='mpg'>"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# -*- coding: utf-8 -*-\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "# read_csv() 함수로 df 생성\n",
        "df = pd.read_csv('./auto-mpg.csv', header=None)\n",
        "\n",
        "# 열 이름을 지정\n",
        "df.columns = ['mpg','cylinders','displacement','horsepower','weight',\n",
        "              'acceleration','model year','origin','name']\n",
        "\n",
        "# 2개의 열을 선택하여 산점도 그리기\n",
        "df.plot(x='weight',y='mpg', kind='scatter')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ZiOiTiHf1uy"
      },
      "source": [
        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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eGI_sXKaf6sj"
      },
      "source": [
        "### 5. 박스 플롯\n",
        "\n",
        "```\n",
        "박스 플롯: DataFrame 객체.plot(kind='box')\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "2XISQacSmMiD",
        "outputId": "b01d317f-9d31-4b01-b805-54c54d2b7a6e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# <예제 3-8> 박스 플롯\n",
        "# -*- coding: utf-8 -*-\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "# read_csv() 함수로 df 생성\n",
        "df = pd.read_csv('./auto-mpg.csv', header=None)\n",
        "\n",
        "# 열 이름을 지정\n",
        "df.columns = ['mpg','cylinders','displacement','horsepower','weight',\n",
        "              'acceleration','model year','origin','name']\n",
        "\n",
        "# 열을 선택하여 박스 플롯 그리기\n",
        "df[['mpg','cylinders']].plot(kind='box')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5yHbnaGYf3WI"
      },
      "source": [
        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