AirRoute.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "AirRoute",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyP3AXs8Uz6IxJpO2v6dFAzD",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/KxxHyoRim/Data-Mining/blob/main/AirRoute.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dd3Y_ZdQ3P9y"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "# from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau\n",
        "import datetime"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hj6mwpknCkcq"
      },
      "source": [
        "## 구글 드라이브 사용 권한 부여"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jAELbvDj3VHv",
        "outputId": "930b1718-c264-43e1-fa4e-10c0e5ff632b"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DQhMtXyVCpKZ"
      },
      "source": [
        "## 구글 드라이브에서 csv 파일 로드"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "DX6HrBW73dMK",
        "outputId": "97ad7cc3-51d4-42c7-ea3b-bccdc1b0bc06"
      },
      "source": [
        "#불러올 파일의 경로를 filename 변수에 저장\n",
        "filename = '/content/drive/My Drive/Data Mining/항공통계.csv'\n",
        "\n",
        "#pandas read_csv로 불러오기\n",
        "data = pd.read_csv(filename, encoding='cp949') # encoding : 한글 로딩 위함\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>항공노선</th>\n",
              "      <th>LEVEL1</th>\n",
              "      <th>값</th>\n",
              "      <th>년(Annual)</th>\n",
              "      <th>증감</th>\n",
              "      <th>증감율</th>\n",
              "      <th>구성비</th>\n",
              "      <th>누계</th>\n",
              "      <th>누계구성비</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>김포-김해</td>\n",
              "      <td>계</td>\n",
              "      <td>273</td>\n",
              "      <td>2020</td>\n",
              "      <td>273</td>\n",
              "      <td>273</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>김포-김해</td>\n",
              "      <td>대한항공</td>\n",
              "      <td>63</td>\n",
              "      <td>2020</td>\n",
              "      <td>63</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>김포-김해</td>\n",
              "      <td>아시아나항공</td>\n",
              "      <td>0</td>\n",
              "      <td>2020</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>김포-김해</td>\n",
              "      <td>제주항공</td>\n",
              "      <td>43</td>\n",
              "      <td>2020</td>\n",
              "      <td>43</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>김포-김해</td>\n",
              "      <td>진에어</td>\n",
              "      <td>56</td>\n",
              "      <td>2020</td>\n",
              "      <td>56</td>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    항공노선  LEVEL1    값  년(Annual)   증감  증감율  구성비  누계  누계구성비\n",
              "0  김포-김해       계  273       2020  273  273    0   0      0\n",
              "1  김포-김해    대한항공   63       2020   63   63    0   0      0\n",
              "2  김포-김해  아시아나항공    0       2020    0    0    0   0      0\n",
              "3  김포-김해    제주항공   43       2020   43   43    0   0      0\n",
              "4  김포-김해     진에어   56       2020   56   56    0   0      0"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IDH54n-HC-Ro"
      },
      "source": [
        "## 내륙라인 값 추출"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "Tr8_iphf36Ai",
        "outputId": "91d0e0bf-5cdd-4e4c-9375-3d3b871632cb"
      },
      "source": [
        "is_inline = data['항공노선'] == '내륙소계'  # '항공노선' column의 값이 '내륙소계'인가\n",
        "is_inline_sum = data['LEVEL1'] == '계'      # 'LEVEL1' column의 값이 '계'인가\n",
        "\n",
        "inline = data[is_inline & is_inline_sum]    # 두 조건을 만족하는 행\n",
        "inline.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>항공노선</th>\n",
              "      <th>LEVEL1</th>\n",
              "      <th>값</th>\n",
              "      <th>년(Annual)</th>\n",
              "      <th>증감</th>\n",
              "      <th>증감율</th>\n",
              "      <th>구성비</th>\n",
              "      <th>누계</th>\n",
              "      <th>누계구성비</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>내륙소계</td>\n",
              "      <td>계</td>\n",
              "      <td>489</td>\n",
              "      <td>2020</td>\n",
              "      <td>489</td>\n",
              "      <td>489</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     항공노선 LEVEL1    값  년(Annual)   증감  증감율  구성비  누계  누계구성비\n",
              "130  내륙소계      계  489       2020  489  489    0   0      0"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pUxr2pJlCvpf"
      },
      "source": [
        "## 제주라인 값 추출"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "odcFHS0EDaXy",
        "outputId": "cfcf2130-78a0-4a0c-938c-c18bb2c49046"
      },
      "source": [
        "is_jeju = data['항공노선'] == '제주소계'  # '항공노선' column의 값이 '제주소계'인가\n",
        "is_jeju_sum = data['LEVEL1'] == '계'      # 'LEVEL1' column의 값이 '계'인가\n",
        "\n",
        "jeju = data[is_jeju & is_jeju_sum]        # 두 조건을 만족하는 행 추출\n",
        "jeju.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>항공노선</th>\n",
              "      <th>LEVEL1</th>\n",
              "      <th>값</th>\n",
              "      <th>년(Annual)</th>\n",
              "      <th>증감</th>\n",
              "      <th>증감율</th>\n",
              "      <th>구성비</th>\n",
              "      <th>누계</th>\n",
              "      <th>누계구성비</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>280</th>\n",
              "      <td>제주소계</td>\n",
              "      <td>계</td>\n",
              "      <td>1592</td>\n",
              "      <td>2020</td>\n",
              "      <td>1592</td>\n",
              "      <td>1592</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     항공노선 LEVEL1     값  년(Annual)    증감   증감율  구성비  누계  누계구성비\n",
              "280  제주소계      계  1592       2020  1592  1592    0   0      0"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "5ZP3X5Ax6Oar",
        "outputId": "80c99caa-03aa-4935-bf95-9b6254515f42"
      },
      "source": [
        "inline_val = inline['값'].reset_index(drop = True)[0]\n",
        "print(\"내륙라인\", inline_val)\n",
        "\n",
        "jeju_val = jeju['값'].reset_index(drop = True)[0]\n",
        "print(\"제주라인\", jeju_val)\n",
        "\n",
        "route_data = [inline_val, jeju_val]\n",
        "label = ['Inland', 'Jeju']\n",
        "x = np.arange(2)\n",
        "\n",
        "plt.bar(x, route_data)\n",
        "plt.xticks(x, label)\n",
        "plt.title('Air Route')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "내륙라인 489\n",
            "제주라인 1592\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}