p_02_3_파일(실).ipynb

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  "metadata": {
    "colab": {
      "name": "p_02_3_파일(실).ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/teacher014/pythonbasic/blob/main/p_02_3_%ED%8C%8C%EC%9D%BC(%EC%8B%A4).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zB5oLSiD0VHt"
      },
      "source": [
        "#파일(File)\n",
        "\n",
        "\n",
        "### **파일 생성**\n",
        "+ 파일 객체 = open(파일 이름, 파일 열기 모드, encoding='UTF-8’)\n",
        " - 파일명이 한글일 경우는 encoding='UTF-8＇을 사용\n",
        "\n",
        "### **파일출력함수 : write()**\n",
        "### **파일입력함수 : readline(), readlines(), read()**\n",
        "+ readline(): 파일의 첫 번째 줄을 읽어 출력하는 경우\n",
        "+ readlines(): 파일의 모든 라인을 읽어서 각각의 줄을 요소로 갖는 리스트로 리턴\n",
        "+ read(): 파일의 내용 전체를 문자열로 리턴\n",
        "\n",
        "### **파일 닫기**\n",
        "+ 파일 객체.close()\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_BCx9kX7jVrS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e0648b3-5d7b-49a0-b374-4a61f77a9026"
      },
      "source": [
        "# 파일 출력\n",
        "f = open('','w')\n",
        "\n",
        "inData = input('기록1: ')\n",
        "\n",
        "inData = input('기록2: ')\n",
        "f.write(inData + '\\n')\n",
        "\n",
        "f.close()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "기록1: 안녕하세요\n",
            "기록2: 반갑습니다\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "63il2_8K919M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8beb0e71-f95d-4f4d-f3b2-415127e00cc2"
      },
      "source": [
        "f = open('/content/test.txt', 'w')\n",
        "inData = input('기록3 : ')\n",
        "f.write(inData)\n",
        "f.close()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "기록할 내용3 : 비가 오려나\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JNQdbiam15Hd"
      },
      "source": [
        "#구글 드라이브 마운트\n",
        "from google.colab import drive \n",
        "drive.mount('/content/gdrive/') "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IM6-HWJ72mss"
      },
      "source": [
        "fp = open('/content/지역평균기온.txt', \n",
        "\n",
        "fp.close()\n",
        "print(data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LSAWTWRW2xwa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90376dfe-7b4e-4ceb-b830-d2c443bff4b7"
      },
      "source": [
        "fp = open('/content/지역평균기온.txt', 'r', encoding='utf-8')\n",
        "\n",
        "fp.close()\n",
        "print(data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "서울,20\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l4Iz230j3S95",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aed62c10-e15e-49fa-e979-b9e96362c7b5"
      },
      "source": [
        "fp = open('/content/지역평균기온.txt', 'r', encoding='utf-8')\n",
        "\n",
        "fp.close()\n",
        "print(data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['서울,18\\n', '부산,17.8\\n', '대구,16.9\\n', '인천,17.8\\n', '백령도,15.5\\n', '전주,19.0\\n', '목포,18.8\\n', '흑산도,16.8\\n', '광주,18.9\\n', '여수,18.4\\n', '대전,18.1\\n', '청주,18.8\\n', '울산,17.2\\n', '포항,18.2\\n', '춘천,15.5\\n', '강릉,14.2\\n', '창원,16.9\\n', '제주,19.7\\n', '울릉도독도,15.2']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YnCzwmI-QHTx"
      },
      "source": [
        "#딕셔너리, # dt[items[0]] = items[1]\n",
        "dt = {}\n",
        "for line in data:\n",
        "  line = #replace\n",
        "  items = #split\n",
        "\n",
        "dt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ykEQwui74KAQ"
      },
      "source": [
        "\n",
        "### **파일을 open한 후 with…as 구문이 끝나면 자동으로 close**\n",
        "```\n",
        "with open(파일 경로, 모드) as 파일 객체:\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X0YIUXkB3iq_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9f3e8bf-6257-48fc-aa3f-db96947c8158"
      },
      "source": [
        "#11월전국기온.txt 파일 읽기\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['서울,18\\n', '부산,17.8\\n', '대구,16.9\\n', '인천,17.8\\n', '백령도,15.5\\n', '전주,19.0\\n', '목포,18.8\\n', '흑산도,16.8\\n', '광주,18.9\\n', '여수,18.4\\n', '대전,18.1\\n', '청주,18.8\\n', '울산,17.2\\n', '포항,18.2\\n', '춘천,15.5\\n', '강릉,14.2\\n', '창원,16.9\\n', '제주,19.7\\n', '울릉도독도,15.2']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8nxJv7KnPGZI"
      },
      "source": [
        "# 리스트로 읽기\n",
        "#item[0], item[1], temp.append(float(item[1]))\n",
        "temp = []\n",
        "for line in data:\n",
        "    line = line.replace('\\n','')\n",
        "    item = line.split(',')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XWHzZv23PKdb"
      },
      "source": [
        "temp"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qX5LPFQXPM8h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8929f672-73e5-449f-f248-3643792c7c0d"
      },
      "source": [
        "print(f'평균기온 : { : 0.2f} ℃')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "평균기온 :  17.46℃\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0l8BuMV-PcA5"
      },
      "source": [
        "## with open() as 파일객체 :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PhT8_QGoPbmk"
      },
      "source": [
        "with open('/content/지역평균기온.txt','r', encoding='utf-8') as fp:\n",
        "  data = fp.readlines()\n",
        "data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qQPkSEc4saw-"
      },
      "source": [
        "**지역, 온도 리스트 만들기**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "idFFXqiUP2H0"
      },
      "source": [
        "#  area.append(item[0]), temp.append(int(item[1]))\n",
        "area = []\n",
        "temp = []\n",
        "\n",
        "for line in data:\n",
        "  line = line.replace('\\n','')\n",
        "  item = line.split(',')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQmRgyppQM1h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea1e3b31-3757-4756-a300-0f3a8179ed53"
      },
      "source": [
        "print(area)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['서울', '부산', '대구', '인천', '광주', '대전', '울산', '세종', '경기', '강원', '충북', '충남', '전북', '전남', '경북', '경남', '제주']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T9iF3ftNQ8if",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e6cf82fe-0b33-4748-ab63-78ad6282ce28"
      },
      "source": [
        "area[0:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['서울', '부산', '대구', '인천', '광주']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hcQmx2SCQSLz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2823fed6-0b37-4d14-b3a2-5b791ece966c"
      },
      "source": [
        "print(temp)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[20, 26, 24, 24, 25, 22, 26, 23, 24, 23, 26, 22, 24, 23, 28, 29, 26]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rCSbv8ZQQVyG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee760160-7368-4442-b3ec-e0f6a76a2a9c"
      },
      "source": [
        "# 리스트 결합 : zip() dict(zip(area, temp))\n",
        "dt = \n",
        "print(dt)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'서울': 20, '부산': 26, '대구': 24, '인천': 24, '광주': 25, '대전': 22, '울산': 26, '세종': 23, '경기': 24, '강원': 23, '충북': 26, '충남': 22, '전북': 24, '전남': 23, '경북': 28, '경남': 29, '제주': 26}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZmR1vdbAQgP0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "e86b40f4-7ed1-42e6-cca6-2e5b3877cd3c"
      },
      "source": [
        "q = input('지역을 입력하세요 : ')\n",
        "print()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "지역을 입력하세요 : 부산\n",
            "26\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dSfPI9DQQsFZ"
      },
      "source": [
        "##쓰기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DVkz0julQvau"
      },
      "source": [
        "#fp.write(area[0])\n",
        "fp = open('지역.txt', 'w', encoding = 'utf-8')\n",
        "\n",
        "fp.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LDPkn1bBRCNF"
      },
      "source": [
        "#  for item in temp:fp.write(str(item) + '\\n')\n",
        "with open('기온.txt', 'w', encoding = 'utf-8') as fp:\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XzmEih0cRQWM"
      },
      "source": [
        "with open('지역.txt', 'w', encoding = 'utf-8') as fp:\n",
        " "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6mBhzQX5TTi4"
      },
      "source": [
        "부산 3월 기온"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N3Sz1i2ATQrN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "ed8fa88b-ecdc-41c3-dfe2-b6107b03e343"
      },
      "source": [
        "# import matplotlib.pyplot as plt\n",
        "#부산 3월 기온\n",
        "temp = [10.8, 8.5, 8.7, 7.9, 5.1, \n",
        "        8.1, 8.2, 10.4, 11, 9.9, \n",
        "        7.8, 9.5, 11.6, 8.2, 7.7, \n",
        "        6.6, 11.1, 12.2, 12.6, 10.4, \n",
        "        13.7, 14.1, 12.6, 11.5, 13, \n",
        "        14.5, 13.7, 9.4, 10.4, 11.6, 12.9]\n",
        "print(f'평균기온 :{sum(temp) / len(temp) : 0.2f} ℃')\n",
        "# plt.plot(temp)\n",
        "# plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "평균기온 : 10.44 ℃\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"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vm02HaTJefqx"
      },
      "source": [
        "## (해결문제1) 지역평균기온.txt 를 딕션너리로 읽어서 그래프 그리기\n",
        "* 해당지역의 평균기온을 알려주는 프로그램을 추가\n",
        "<p> - 파일읽기, 그래프는 함수로 처리 \n",
        "<p> - 단, 지역입력에 q가 입력되면, 종료후에 그래프 표시\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Go889Tl6RHUE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f6aabe0-1a9f-40a3-d403-d80349e11c11"
      },
      "source": [
        "#한글 폰트 사용 : colab , 런타임 다시 시작 하기\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "!apt -qq -y install fonts-nanum"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fonts-nanum is already the newest version (20170925-1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 37 not upgraded.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BWzqbOKnQ-Yg"
      },
      "source": [
        "#시각화 모듈\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#한글 폰트 사용 : vscode , window\n",
        "import matplotlib as mpl\n",
        "import matplotlib.font_manager as fm\n",
        "fontpath = '/usr/share/fonts/truetype/nanum/NanumBarunGothic.ttf'\n",
        "font = fm.FontProperties(fname=fontpath, size=9)\n",
        "plt.rc('font', family='NanumBarunGothic') \n",
        "mpl.font_manager._rebuild()\n",
        "\n",
        "#마이너스 깨지는 경우 처리\n",
        "matplotlib.rcParams['axes.unicode_minus'] = False \n",
        "\n",
        "#새 창 띄우기(colab에서는 안됨)\n",
        "import matplotlib as tk"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MvGm50YLknhZ"
      },
      "source": [
        "#파일읽기\n",
        "def readData(fname):\n",
        "   #----------------------------------------------------------------------------\n",
        "   # 매개변수로 파일명만 받으므로 경로를 넣어서 파일을 읽을 수 있도록 변수를 조정\n",
        "   #----------------------------------------------------------------------------  \n",
        "    fname = \"/content/\" + fname\n",
        "    fp = open(fname, 'r', encoding='utf-8')\n",
        "    data = fp.readlines()\n",
        "    fp.close()\n",
        "\n",
        "    return data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DbHsgi2Y8vd_"
      },
      "source": [
        "data = readData('지역평균기온.txt')\n",
        "data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fTrg0943hSPt"
      },
      "source": [
        "#------------------------------------------------------------------------------- \n",
        "# 파일로 읽은 데이터 -> 딕션너리로 분리\n",
        "#-------------------------------------------------------------------------------\n",
        "def showdt(indata):\n",
        "    dt = {}\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NlMWduDrlEcZ"
      },
      "source": [
        "dt = showdt(data)\n",
        "dt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "axywSisIOrN0"
      },
      "source": [
        "#------------------------------------------------------------------------------- \n",
        "# 딕션너리의 데이터를 이용해서 그래프 그리기\n",
        "# 그래프 파일로 저장하는 코드 추가\n",
        "#-------------------------------------------------------------------------------\n",
        "def show(dt):\n",
        "    #import matplotlib.pyplot as plt\n",
        "    #plt.figure(figsize=(10, 5))\n",
        "    \n",
        "    x = list(dt.keys())\n",
        "    y = \n",
        "\n",
        "    # plt.plot(x,y,'go--', color='skyblue')\n",
        "    # plt.title('지역평균기온')\n",
        "    # plt.xticks(rotation=0)\n",
        "    # plt.draw() \n",
        "    \n",
        "    # fig = plt.gcf()\n",
        "    # fig.savefig('/content/myfile.png', dpi=fig.dpi)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1yQPEV5RgfNc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "62767307-e84e-40de-9170-449c594f7d1d"
      },
      "source": [
        "#------------------------------------------------------------------------------- \n",
        "# 각 모듈을 함수로 만들고 메인을 작성하여 사용\n",
        "# 해당 지역 평균 기온 알려주는 코드 추가\n",
        "#-------------------------------------------------------------------------------\n",
        "if __name__ == \"__main__\":\n",
        "    data = readData('지역평균기온.txt')\n",
        "    dt = showdt(data)\n",
        "    print(dt)\n",
        "    while True :\n",
        "\n",
        "\n",
        "\n",
        "    show(dt)\n",
        "  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'서울': 20, '부산': 26, '대구': 24, '인천': 24, '광주': 25, '대전': 22, '울산': 26, '세종': 23, '경기': 24, '강원': 23, '충북': 26, '충남': 22, '전북': 24, '전남': 23, '경북': 28, '경남': 29, '제주': 26}\n",
            "지역을 입력하세요 : q\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HjHNgefHepOQ"
      },
      "source": [
        "# 나눔 글꼴 설치 # 설치후 런타임 재실행\n",
        "!sudo apt-get install -y fonts-nanum\n",
        "!sudo fc-cache -fv\n",
        "!rm ~/.cache/matplotlib -rf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kPq67jXxevPM"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.rc('font', family='NanumBarunGothic')"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}